LLM Research Papers: The 2024 List

It’s been a very eventful and exciting year in AI research. This is especially true if you are interested in LLMs.

I had big plans for this December edition and was planning to publish a new article with a discussion of all my research highlights from 2024. I still plan to do so, but due to an accident and serious injury, I am currently unable to work at a computer and finish the draft. But I hope to recover in the upcoming weeks and be back on my feet soon.

In the meantime, I want to share my running bookmark list of many fascinating (mostly LLM-related) papers I stumbled upon in 2024. It’s just a list, but maybe it will come in handy for those who are interested in finding some gems to read for the holidays.

And if you are interested in more code-heavy reading and tinkering, My Build A Large Language Model (From Scratch) book is out on Amazon as of last month.

In addition, I added a lot of bonus materials to the GitHub repository.

Thanks for your understanding and support, and I hope to make a full recovery soon and be back with the Research Highlights 2024 article in a few weeks!

• 1 Jan, Astraios: Parameter-Efficient Instruction Tuning Code Large Language Models, https://arxiv.org/abs/2401.00788

• 2 Jan, A Comprehensive Study of Knowledge Editing for Large Language Models, https://arxiv.org/abs/2401.01286

• 2 Jan, LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuning, https://arxiv.org/abs/2401.01325

• 2 Jan, Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models, https://arxiv.org/abs/2401.01335

• 2 Jan, LLaMA Beyond English: An Empirical Study on Language Capability Transfer, https://arxiv.org/abs/2401.01055

• 3 Jan, A Mechanistic Understanding of Alignment Algorithms: A Case Study on DPO and Toxicity, https://arxiv.org/abs/2401.01967

• 4 Jan, LLaMA Pro: Progressive LLaMA with Block Expansion, https://arxiv.org/abs/2401.02415

• 4 Jan, LLM Augmented LLMs: Expanding Capabilities through Composition, https://arxiv.org/abs/2401.02412

• 4 Jan, Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM, https://arxiv.org/abs/2401.02994

• 5 Jan, DeepSeek LLM: Scaling Open-Source Language Models with Longtermism, https://arxiv.org/abs/2401.02954

• 5 Jan, Denoising Vision Transformers, https://arxiv.org/abs/2401.02957

• 7 Jan, Soaring from 4K to 400K: Extending LLM’s Context with Activation Beacon, https://arxiv.org/abs/2401.03462

• 8 Jan, Mixtral of Experts, https://arxiv.org/abs/2401.04088

• 8 Jan, MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts, https://arxiv.org/abs/2401.04081

• 8 Jan, A Minimaximalist Approach to Reinforcement Learning from Human Feedback, https://arxiv.org/abs/2401.04056

• 9 Jan, RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust Adaptation, https://arxiv.org/abs/2401.04679

• 10 Jan, Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training, https://arxiv.org/abs/2401.05566

• 11 Jan, Transformers are Multi-State RNNs, https://arxiv.org/abs/2401.06104

• 11 Jan, A Closer Look at AUROC and AUPRC under Class Imbalance, https://arxiv.org/abs/2401.06091

• 12 Jan, An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models, https://arxiv.org/abs/2401.06692

• 16 Jan, Tuning Language Models by Proxy, https://arxiv.org/abs/2401.08565

• 16 Jan, Scalable Pre-training of Large Autoregressive Image Models, https://arxiv.org/abs/2401.08541

• 16 Jan, Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering, https://arxiv.org/abs/2401.08500

• 16 Jan, RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture, https://arxiv.org/abs/2401.08406

• 17 Jan, ReFT: Reasoning with Reinforced Fine-Tuning, https://arxiv.org/abs/2401.08967

• 18 Jan, DiffusionGPT: LLM-Driven Text-to-Image Generation System, https://arxiv.org/abs/2401.10061

• 18 Jan, Self-Rewarding Language Models, https://arxiv.org/abs/2401.10020

• 18 Jan, VMamba: Visual State Space Model, https://arxiv.org/abs/2401.10166

• 19 Jan, Knowledge Fusion of Large Language Models, https://arxiv.org/abs/2401.10491

• 22 Jan, SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities, https://arxiv.org/abs/2401.12168

• 22 Jan, WARM: On the Benefits of Weight Averaged Reward Models, https://arxiv.org/abs/2401.12187

• 22 Jan, Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text, https://arxiv.org/abs/2401.12070

• 24 Jan, MambaByte: Token-free Selective State Space Model, https://arxiv.org/abs/2401.13660

• 24 Jan, SpacTor-T5: Pre-training T5 Models with Span Corruption and Replaced Token Detection, https://arxiv.org/abs/2401.13160

• 25 Jan, Rethinking Patch Dependence for Masked Autoencoders, https://arxiv.org/abs/2401.14391

• 25 Jan, Pix2gestalt: Amodal Segmentation by Synthesizing Wholes, https://arxiv.org/abs/2401.14398

• 25 Jan, Multimodal Pathway: Improve Transformers with Irrelevant Data from Other Modalities, https://arxiv.org/abs/2401.14405

• 26 Jan, EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty, https://arxiv.org/abs/2401.15077

• 29 Jan, MoE-LLaVA: Mixture of Experts for Large Vision-Language Models, https://arxiv.org/abs/2401.15947

• 29 Jan, Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modeling, https://arxiv.org/abs/2401.16380

• 31 Jan, KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization, https://arxiv.org/abs/2401.18079

• 1 Feb, Efficient Exploration for LLMs, https://arxiv.org/abs/2402.00396

• 1 Feb, OLMo: Accelerating the Science of Language Models, https://arxiv.org/abs/2402.00838

• 1 Feb, Tiny Titans: Can Smaller Large Language Models Punch Above Their Weight in the Real World for Meeting Summarization?, https://arxiv.org/abs/2402.00841

• 1 Feb, Repeat After Me: Transformers are Better than State Space Models at Copying, https://arxiv.org/abs/2402.01032

• 2 Feb, LiPO: Listwise Preference Optimization through Learning-to-Rank, https://arxiv.org/abs/2402.01878

• 2 Feb, FindingEmo: An Image Dataset for Emotion Recognition in the Wild, https://arxiv.org/abs/2402.01355

• 3 Feb, More Agents Is All You Need, https://arxiv.org/abs/2402.05120

• 5 Feb, DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models, https://arxiv.org/abs/2402.03300

• 6 Feb, MobileVLM V2: Faster and Stronger Baseline for Vision Language Model, https://arxiv.org/abs/2402.03766

• 6 Feb, A Phase Transition Between Positional and Semantic Learning in a Solvable Model of Dot-Product Attention, https://arxiv.org/abs/2402.03902

• 6 Feb, Scaling Laws for Downstream Task Performance of Large Language Models, https://arxiv.org/abs/2402.04177

• 6 Feb, MOMENT: A Family of Open Time-series Foundation Models, https://arxiv.org/abs/2402.03885

• 6 Feb, Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models, https://arxiv.org/abs/2402.03749

• 6 Feb, Self-Discover: Large Language Models Self-Compose Reasoning Structures, https://arxiv.org/abs/2402.03620

• 7 Feb, Grandmaster-Level Chess Without Search, https://arxiv.org/abs/2402.04494

• 7 Feb, Direct Language Model Alignment from Online AI Feedback, https://arxiv.org/abs/2402.04792

• 8 Feb, Buffer Overflow in Mixture of Experts, https://arxiv.org/abs/2402.05526

• 9 Feb, The Boundary of Neural Network Trainability is Fractal, https://arxiv.org/abs/2402.06184

• 11 Feb, ODIN: Disentangled Reward Mitigates Hacking in RLHF, https://arxiv.org/abs/2402.07319

• 12 Feb, Policy Improvement using Language Feedback Models, https://arxiv.org/abs/2402.07876

• 12 Feb, Scaling Laws for Fine-Grained Mixture of Experts, https://arxiv.org/abs/2402.07871

• 12 Feb, Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model, https://arxiv.org/abs/2402.07610

• 12 Feb, Step-On-Feet Tuning: Scaling Self-Alignment of LLMs via Bootstrapping, https://arxiv.org/abs/2402.07610

• 12 Feb, Suppressing Pink Elephants with Direct Principle Feedback, https://arxiv.org/abs/2402.07896

• 13 Feb, World Model on Million-Length Video And Language With RingAttention, https://arxiv.org/abs/2402.08268

• 13 Feb, Mixtures of Experts Unlock Parameter Scaling for Deep RL, https://arxiv.org/abs/2402.08609

• 14 Feb, DoRA: Weight-Decomposed Low-Rank Adaptation, https://arxiv.org/abs/2402.09353

• 14 Feb, Transformers Can Achieve Length Generalization But Not Robustly, https://arxiv.org/abs/2402.09371

• 15 Feb, BASE TTS: Lessons From Building a Billion-Parameter Text-to-Speech Model on 100K Hours of Data, https://arxiv.org/abs/2402.08093

• 15 Feb, Recovering the Pre-Fine-Tuning Weights of Generative Models, https://arxiv.org/abs/2402.10208

• 15 Feb, Generative Representational Instruction Tuning, https://arxiv.org/abs/2402.09906

• 16 Feb, FinTral: A Family of GPT-4 Level Multimodal Financial Large Language Models, https://arxiv.org/abs/2402.10986

• 17 Feb, OneBit: Towards Extremely Low-bit Large Language Models, https://arxiv.org/abs/2402.11295

• 18 Feb, LongAgent: Scaling Language Models to 128k Context through Multi-Agent Collaboration, https://arxiv.org/abs/2402.11550

• 19 Feb, Reformatted Alignment, https://arxiv.org/abs/2402.12219

• 19 Feb, AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling, https://arxiv.org/abs/2402.12226

• 19 Feb, Towards Cross-Tokenizer Distillation: the Universal Logit Distillation Loss for LLMs, https://arxiv.org/abs/2402.12030

• 19 Feb, LoRA+: Efficient Low Rank Adaptation of Large Models, https://arxiv.org/abs/2402.12354

• 20 Feb, Neural Network Diffusion, https://arxiv.org/abs/2402.13144

• 21 Feb, YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information, https://arxiv.org/abs/2402.13616

• 21 Feb, LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens, https://arxiv.org/abs/2402.13753

• 21 Feb, Large Language Models for Data Annotation: A Survey, https://arxiv.org/abs/2402.13446

• 22 Feb, TinyLLaVA: A Framework of Small-scale Large Multimodal Models, https://arxiv.org/abs/2402.14289

• 22 Feb, Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs, https://arxiv.org/abs/2402.14740

• 23 Feb, Genie: Generative Interactive Environments, https://arxiv.org/abs/2402.15391

• 27 Feb, The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits, https://arxiv.org/abs/2402.17764

• 27 Feb, Sora Generates Videos with Stunning Geometrical Consistency, https://arxiv.org/abs/2402.17403

• 27 Feb, When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method, https://arxiv.org/abs/2402.17193

• 29 Feb, Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models, https://arxiv.org/abs/2402.19427

• 1 Mar, Learning and Leveraging World Models in Visual Representation Learning, https://arxiv.org/abs/2403.00504

• 3 Mar, Improving LLM Code Generation with Grammar Augmentation, https://arxiv.org/abs/2403.01632

• 3 Mar, The Hidden Attention of Mamba Models, https://arxiv.org/abs/2403.01590

• 4 Mar, Training-Free Pretrained Model Merging, https://arxiv.org/abs/2403.01753

• 4 Mar, Vision-RWKV: Efficient and Scalable Visual Perception with RWKV-Like Architectures, https://arxiv.org/abs/2403.02308

• 5 Mar, The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning, https://arxiv.org/abs/2403.03218

• 5 Mar, Evolution Transformer: In-Context Evolutionary Optimization, https://arxiv.org/abs/2403.02985

• 5 Mar, Enhancing Vision-Language Pre-training with Rich Supervisions, https://arxiv.org/abs/2403.03346

• 5 Mar, Scaling Rectified Flow Transformers for High-Resolution Image Synthesis, https://arxiv.org/abs/2403.03206

• 5 Mar, Design2Code: How Far Are We From Automating Front-End Engineering?, https://arxiv.org/abs/2403.03163

• 6 Mar, ShortGPT: Layers in Large Language Models are More Redundant Than You Expect, https://arxiv.org/abs/2403.03853

• 6 Mar, Backtracing: Retrieving the Cause of the Query, https://arxiv.org/abs/2403.03956

• 6 Mar, Learning to Decode Collaboratively with Multiple Language Models, https://arxiv.org/abs/2403.03870

• 6 Mar, SaulLM-7B: A pioneering Large Language Model for Law, https://arxiv.org/abs/2403.03883

• 6 Mar, Are Language Models Puzzle Prodigies? Algorithmic Puzzles Unveil Serious Challenges in Multimodal Reasoning, https://arxiv.org/abs/2403.03864

• 6 Mar, 3D Diffusion Policy, https://arxiv.org/abs/2403.03954

• 6 Mar, MedMamba: Vision Mamba for Medical Image Classification, https://arxiv.org/abs/2403.03849

• 6 Mar, GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection, https://arxiv.org/abs/2403.03507

• 6 Mar, Stop Regressing: Training Value Functions via Classification for Scalable Deep RL, https://arxiv.org/abs/2403.03950

• 7 Mar, How Far Are We from Intelligent Visual Deductive Reasoning?, https://arxiv.org/abs/2403.04732

• 7 Mar, Common 7B Language Models Already Possess Strong Math Capabilities, https://arxiv.org/abs/2403.04706

• 8 Mar, Gemini 1.5: Unlocking Multimodal Understanding Across Millions of Tokens of Context, https://arxiv.org/abs/2403.05530

• 8 Mar, Is Cosine-Similarity of Embeddings Really About Similarity?, https://arxiv.org/abs/2403.05440

• 8 Mar, LLM4Decompile: Decompiling Binary Code with Large Language Models, https://arxiv.org/abs/2403.05286

• 9 Mar, Algorithmic Progress in Language Models, https://arxiv.org/abs/2403.05812

• 11 Mar, Stealing Part of a Production Language Model, https://arxiv.org/abs/2403.06634

• 12 Mar, Chronos: Learning the Language of Time Series, https://arxiv.org/abs/2403.07815

• 13 Mar, Simple and Scalable Strategies to Continually Pre-train Large Language Models, https://arxiv.org/abs/2403.08763

• 13 Mar, Language Models Scale Reliably With Over-Training and on Downstream Tasks, https://arxiv.org/abs/2403.08540

• 14 Mar, BurstAttention: An Efficient Distributed Attention Framework for Extremely Long Sequences, https://arxiv.org/abs/2403.09347

• 14 Mar, LocalMamba: Visual State Space Model with Windowed Selective Scan, https://arxiv.org/abs/2403.09338

• 14 Mar, GiT: Towards Generalist Vision Transformer through Universal Language Interface, https://arxiv.org/abs/2403.09394

• 14 Mar, MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training, https://arxiv.org/abs/2403.09611

• 15 Mar, RAFT: Adapting Language Model to Domain Specific RAG, https://arxiv.org/abs/2403.10131

• 18 Mar, TnT-LLM: Text Mining at Scale with Large Language Models, https://arxiv.org/abs/2403.12173

• 18 Mar, Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression, https://arxiv.org/abs/2403.15447

• 19 Mar, PERL: Parameter Efficient Reinforcement Learning from Human Feedback, https://arxiv.org/abs/2403.10704

• 20 Mar, RewardBench: Evaluating Reward Models for Language Modeling, https://arxiv.org/abs/2403.13787

• 20 Mar, LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models, https://arxiv.org/abs/2403.13372

• 21 Mar, RakutenAI-7B: Extending Large Language Models for Japanese, https://arxiv.org/abs/2403.15484

• 22 Mar, SiMBA: Simplified Mamba-Based Architecture for Vision and Multivariate Time Series, https://arxiv.org/abs/2403.15360

• 22 Mar, Can Large Language Models Explore In-Context?, https://arxiv.org/abs/2403.15371

• 22 Mar, LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement, https://arxiv.org/abs/2403.15042

• 25 Mar, LLM Agent Operating System, https://arxiv.org/abs/2403.16971

• 26 Mar, The Unreasonable Ineffectiveness of the Deeper Layers, https://arxiv.org/abs/2403.17887

• 27 Mar, BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text, https://arxiv.org/abs/2403.18421

• 27 Mar, ViTAR: Vision Transformer with Any Resolution, https://arxiv.org/abs/2403.18361

• 27 Mar, Long-form Factuality in Large Language Models, https://arxiv.org/abs/2403.18802

• 27 Mar, Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models, https://arxiv.org/abs/2403.18814

• 26 Mar, LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning, https://arxiv.org/abs/2403.17919

• 26 Mar, Mechanistic Design and Scaling of Hybrid Architectures, https://arxiv.org/abs/2403.17844

• 28 Mar, MagicLens: Self-Supervised Image Retrieval with Open-Ended Instructions, https://arxiv.org/abs/2403.19651

• 28 Mar, Model Stock: All We Need Is Just a Few Fine-Tuned Models, https://arxiv.org/abs/2403.19522

• 1 Apr, Do Language Models Plan Ahead for Future Tokens?, https://arxiv.org/abs/2404.00859

• 1 Apr, Bigger is not Always Better: Scaling Properties of Latent Diffusion Models, https://arxiv.org/abs/2404.01367

• 1 Apr, The Fine Line: Navigating Large Language Model Pretraining with Down-streaming Capability Analysis, https://arxiv.org/abs/2404.01204

• 1 Apr, Diffusion-RWKV: Scaling RWKV-Like Architectures for Diffusion Models, https://arxiv.org/abs/2404.04478

• 2 Apr, Mixture-of-Depths: Dynamically Allocating Compute in Transformer-Based Language Models, https://arxiv.org/abs/2404.02258

• 2 Apr, Long-context LLMs Struggle with Long In-context Learning, https://arxiv.org/abs/2404.02060

• 2 Apr, Emergent Abilities in Reduced-Scale Generative Language Models, https://arxiv.org/abs/2404.02204

• 2 Apr, Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks, https://arxiv.org/abs/2404.02151

• 3 Apr, On the Scalability of Diffusion-based Text-to-Image Generation, https://arxiv.org/abs/2404.02883

• 3 Apr, BAdam: A Memory Efficient Full Parameter Training Method for Large Language Models, https://arxiv.org/abs/2404.02827

• 3 Apr, Cross-Attention Makes Inference Cumbersome in Text-to-Image Diffusion Models, https://arxiv.org/abs/2404.02747

• 4 Apr, Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences, https://arxiv.org/abs/2404.02151

• 4 Apr, Training LLMs over Neurally Compressed Text, https://arxiv.org/abs/2404.03626

• 4 Apr, CantTalkAboutThis: Aligning Language Models to Stay on Topic in Dialogues, https://arxiv.org/abs/2404.03820

• 5 Apr, ReFT: Representation Finetuning for Language Models, https://arxiv.org/abs/2404.03592

• 5 Apr, Verifiable by Design: Aligning Language Models to Quote from Pre-Training Data, https://arxiv.org/abs/2404.03862

• 5 Apr, Sigma: Siamese Mamba Network for Multi-Modal Semantic Segmentation, https://arxiv.org/abs/2404.04256

• 8 Apr, AutoCodeRover: Autonomous Program Improvement, https://arxiv.org/abs/2404.05427

• 8 Apr, Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence, https://arxiv.org/abs/2404.05892

• 8 Apr, CodecLM: Aligning Language Models with Tailored Synthetic Data, https://arxiv.org/abs/2404.05875

• 9 Apr, MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies, https://arxiv.org/abs/2404.06395

• 9 Apr, Elephants Never Forget: Memorization and Learning of Tabular Data in Large Language Models, https://arxiv.org/abs/2404.06209

• 9 Apr, LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders, https://arxiv.org/abs/2404.05961

• 10 Apr, Adapting LLaMA Decoder to Vision Transformer, https://arxiv.org/abs/2404.06773

• 10 Apr, Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention, https://arxiv.org/abs/2404.07143

• 11 Apr, LLoCO: Learning Long Contexts Offline, https://arxiv.org/abs/2404.07979

• 11 Apr, JetMoE: Reaching Llama2 Performance with 0.1M Dollars, https://arxiv.org/abs/2404.07413

• 11 Apr, Best Practices and Lessons Learned on Synthetic Data for Language Models, https://arxiv.org/abs/2404.07503

• 11 Apr, Rho-1: Not All Tokens Are What You Need, https://arxiv.org/abs/2404.07965

• 12 Apr, Pre-training Small Base LMs with Fewer Tokens, https://arxiv.org/abs/2404.08634

• 12 Apr, Dataset Reset Policy Optimization for RLHF, https://arxiv.org/abs/2404.08495

• 13 Apr, LLM In-Context Recall is Prompt Dependent, https://arxiv.org/abs/2404.08865

• 15 Apr, State Space Model for New-Generation Network Alternative to Transformers: A Survey, https://arxiv.org/abs/2404.09516

• 15 Apr, Chinchilla Scaling: A Replication Attempt, https://arxiv.org/abs/2404.10102

• 15 Apr, Learn Your Reference Model for Real Good Alignment, https://arxiv.org/abs/2404.09656

• 16 Apr, Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study, https://arxiv.org/abs/2404.10719

• 16 Apr, Scaling (Down) CLIP: A Comprehensive Analysis of Data, Architecture, and Training Strategies, https://arxiv.org/abs/2404.08197

• 16 Apr, How Faithful Are RAG Models? Quantifying the Tug-of-War Between RAG and LLMs' Internal Prior, https://arxiv.org/abs/2404.10198

• 17 Apr, A Survey on Retrieval-Augmented Text Generation for Large Language Models, https://arxiv.org/abs/2404.10981

• 18 Apr, When LLMs are Unfit Use FastFit: Fast and Effective Text Classification with Many Classes, https://arxiv.org/abs/2404.12365

• 18 Apr, Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing, https://arxiv.org/abs/2404.12253

• 18 Apr, OpenBezoar: Small, Cost-Effective and Open Models Trained on Mixes of Instruction Data, https://arxiv.org/abs/2404.12195

• 19 Apr, The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions, https://arxiv.org/abs/2404.13208

• 22 Apr, How Good Are Low-bit Quantized LLaMA3 Models? An Empirical Study, https://arxiv.org/abs/2404.14047

• 22 Apr, Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone, https://arxiv.org/abs/2404.14219

• 22 Apr, OpenELM: An Efficient Language Model Family with Open-source Training and Inference Framework, https://arxiv.org/abs/2404.14619

• 22 Apr, A Survey on Self-Evolution of Large Language Models, https://arxiv.org/abs/2404.14662

• 23 Apr, Multi-Head Mixture-of-Experts, https://arxiv.org/abs/2404.15045

• 23 Apr, NExT: Teaching Large Language Models to Reason about Code Execution, https://arxiv.org/abs/2404.14662

• 23 Apr, Graph Machine Learning in the Era of Large Language Models (LLMs), https://arxiv.org/abs/2404.14928

• 24 Apr, Retrieval Head Mechanistically Explains Long-Context Factuality, https://arxiv.org/abs/2404.15574

• 25 Apr, Layer Skip: Enabling Early Exit Inference and Self-Speculative Decoding, https://arxiv.org/abs/2404.16710

• 25 Apr, Make Your LLM Fully Utilize the Context, https://arxiv.org/abs/2404.16811

• 28 Apr, LoRA Land: 310 Fine-tuned LLMs that Rival GPT-4, A Technical Report, https://arxiv.org/abs/2405.00732

• 30 Apr, Better & Faster Large Language Models via Multi-token Prediction, https://arxiv.org/abs/2404.19737

• 30 Apr, RAG and RAU: A Survey on Retrieval-Augmented Language Model in Natural Language Processing, https://arxiv.org/abs/2404.19543

• 30 Apr, A Primer on the Inner Workings of Transformer-based Language Models, https://arxiv.org/abs/2405.00208

• 30 Apr, When to Retrieve: Teaching LLMs to Utilize Information Retrieval Effectively, https://arxiv.org/abs/2404.19705

• 30 Apr, KAN: Kolmogorov–Arnold Networks, https://arxiv.org/abs/2404.19756

• 1 May, Is Bigger Edit Batch Size Always Better? An Empirical Study on Model Editing with Llama-3, https://arxiv.org/abs/2405.00664

• 1 May, Self-Play Preference Optimization for Language Model Alignment, https://arxiv.org/abs/2405.00675

• 1 May, A Careful Examination of Large Language Model Performance on Grade School Arithmetic, https://arxiv.org/abs/2405.00332

• 2 May, Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models, https://arxiv.org/abs/2405.01535

• 3 May, What Matters When Building Vision-Language Models?, https://arxiv.org/abs/2405.02246

• 5 May, Is Flash Attention Stable?, https://arxiv.org/abs/2405.02803

• 7 May, vAttention: Dynamic Memory Management for Serving LLMs without PagedAttention, https://arxiv.org/abs/2405.04437

• 7 May, xLSTM: Extended Long Short-Term Memory, https://arxiv.org/abs/2405.04517

• 8 May, You Only Cache Once: Decoder-Decoder Architectures for Language Models, https://arxiv.org/abs/2405.05254

• 8 May, DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model, https://arxiv.org/abs/2405.04434

• 8 May, Fishing for Magikarp: Automatically Detecting Under-trained Tokens in Large Language Models, https://arxiv.org/abs/2405.05417

• 9 May, Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?, https://arxiv.org/abs/2405.05904

• 10 May, Value Augmented Sampling for Language Model Alignment and Personalization, https://arxiv.org/abs/2405.06639

• 12 May, PHUDGE: Phi-3 as Scalable Judge, https://arxiv.org/abs/2405.08029

• 13 May, RLHF Workflow: From Reward Modeling to Online RLHF, https://arxiv.org/abs/2405.07863

• 15 May, LoRA Learns Less and Forgets Less, https://arxiv.org/abs/2405.09673

• 15 May, Xmodel-VLM: A Simple Baseline for Multimodal Vision Language Model, https://arxiv.org/abs/2405.09215

• 16 May, Chameleon: Mixed-Modal Early-Fusion Foundation Models, https://arxiv.org/abs/2405.09818

• 17 May, Towards Modular LLMs by Building and Reusing a Library of LoRAs, https://arxiv.org/abs/2405.11157

• 19 May, SLAB: Efficient Transformers with Simplified Linear Attention and Progressive Re-parameterized Batch Normalization, https://arxiv.org/abs/2405.11582

• 20 May, MoRA: High-Rank Updating for Parameter-Efficient Fine-Tuning, https://arxiv.org/abs/2405.12130

• 22 May, Attention as an RNN, https://arxiv.org/abs/2405.13956

• 22 May, Dense Connector for MLLMs, https://arxiv.org/abs/2405.13800

• 23 May, AlignGPT: Multi-modal Large Language Models with Adaptive Alignment Capability, https://arxiv.org/abs/2405.14129

• 23 May, SimPO: Simple Preference Optimization with a Reference-Free Reward, https://arxiv.org/abs/2405.14734

• 23 May, Instruction Tuning With Loss Over Instructions, https://arxiv.org/abs/2405.14394

• 24 May, The Road Less Scheduled, https://arxiv.org/abs/2405.15682

• 26 May, Stacking Your Transformers: A Closer Look at Model Growth for Efficient LLM Pre-Training, https://arxiv.org/abs/2405.15319

• 26 May, gzip Predicts Data-dependent Scaling Laws, https://arxiv.org/abs/2405.16684

• 27 May, Trans-LoRA: Towards Data-free Transferable Parameter Efficient Finetuning, https://arxiv.org/abs/2405.17258

• 28 May, VeLoRA: Memory Efficient Training using Rank-1 Sub-Token Projections, https://arxiv.org/abs/2405.17991

• 28 May, LLaMA-NAS: Efficient Neural Architecture Search for Large Language Models, https://arxiv.org/abs/2405.18377

• 29 May, Contextual Position Encoding: Learning to Count What's Important, https://arxiv.org/abs/2405.18719

• 2 Jun, Show, Don't Tell: Aligning Language Models with Demonstrated Feedback, https://arxiv.org/abs/2406.00888

• 3 Jun, Skywork-MoE: A Deep Dive into Training Techniques for Mixture-of-Experts Language Models, https://arxiv.org/abs/2406.06563

• 3 Jun, OLoRA: Orthonormal Low-Rank Adaptation of Large Language Models, https://arxiv.org/abs/2406.01775

• 3 Jun, The Geometry of Categorical and Hierarchical Concepts in Large Language Models, https://arxiv.org/abs/2406.01506

• 3 Jun, Towards Scalable Automated Alignment of LLMs: A Survey, https://arxiv.org/abs/2406.01252

• 4 Jun, Scalable MatMul-free Language Modeling, https://arxiv.org/abs/2406.02528

• 4 Jun, Block Transformer: Global-to-Local Language Modeling for Fast Inference, https://arxiv.org/abs/2406.02657

• 6 Jun, Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models, https://arxiv.org/abs/2406.04271

• 6 Jun, The Prompt Report: A Systematic Survey of Prompting Techniques, https://arxiv.org/abs/2406.06608

• 6 Jun, Transformers Need Glasses! Information Over-Squashing in Language Tasks, https://arxiv.org/abs/2406.04267

• 6 Jun, Are We Done with MMLU?, https://arxiv.org/abs/2406.04127

• 6 Jun, Step-aware Preference Optimization: Aligning Preference with Denoising Performance at Each Step, https://arxiv.org/abs/2406.04314

• 7 Jun, Boosting Large-scale Parallel Training Efficiency with C4: A Communication-Driven Approach, https://arxiv.org/abs/2406.04594

• 7 Jun, CRAG -- Comprehensive RAG Benchmark, https://arxiv.org/abs/2406.04744

• 7 Jun, WildBench: Benchmarking LLMs with Challenging Tasks from Real Users in the Wild, https://arxiv.org/abs/2406.04770

• 7 Jun, Mixture-of-Agents Enhances Large Language Model Capabilities, https://arxiv.org/abs/2406.04692

• 7 Jun, BERTs are Generative In-Context Learners, https://arxiv.org/abs/2406.04823

• 7 Jun, 3D-GRAND: A Million-Scale Dataset for 3D-LLMs with Better Grounding and Less Hallucination, https://arxiv.org/abs/2406.05132

• 8 Jun, Creativity Has Left the Chat: The Price of Debiasing Language Models, https://arxiv.org/abs/2406.05587

• 10 Jun, Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation, https://arxiv.org/abs/2406.06525

• 10 Jun, Margin-aware Preference Optimization for Aligning Diffusion Models Without Reference, https://arxiv.org/abs/2406.06424

• 10 Jun, Husky: A Unified, Open-Source Language Agent for Multi-Step Reasoning, https://arxiv.org/abs/2406.06469

• 10 Jun, Turbo Sparse: Achieving LLM SOTA Performance with Minimal Activated Parameters, https://arxiv.org/abs/2406.05955

• 10 Jun, Self-Tuning: Instructing LLMs to Effectively Acquire New Knowledge through Self-Teaching, https://arxiv.org/abs/2406.06326

• 11 Jun, An Image is Worth 32 Tokens for Reconstruction and Generation, https://arxiv.org/abs/2406.07550

• 11 Jun, TextGrad: Automatic "Differentiation" via Text, https://arxiv.org/abs/2406.07496

• 11 Jun, Simple and Effective Masked Diffusion Language Models, https://arxiv.org/abs/2406.07524

• 11 Jun, Never Miss A Beat: An Efficient Recipe for Context Window Extension of Large Language Models with Consistent "Middle" Enhancement, https://arxiv.org/abs/2406.07138

• 11 Jun, Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling, https://arxiv.org/abs/2406.07522

• 12 Jun, Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing, https://arxiv.org/abs/2406.08464

• 12 Jun, What If We Recaption Billions of Web Images with LLaMA-3?, https://arxiv.org/abs/2406.08478

• 12 Jun, Large Language Model Unlearning via Embedding-Corrupted Prompts, https://arxiv.org/abs/2406.07933

• 12 Jun, Large Language Models Must Be Taught to Know What They Don't Know, https://arxiv.org/abs/2406.08391

• 12 Jun, An Empirical Study of Mamba-based Language Models, https://arxiv.org/abs/2406.07887

• 12 Jun, Discovering Preference Optimization Algorithms with and for Large Language Models, https://arxiv.org/abs/2406.08414

• 13 Jun, Transformers Meet Neural Algorithmic Reasoners, https://arxiv.org/abs/2406.09308

• 13 Jun, MLKV: Multi-Layer Key-Value Heads for Memory Efficient Transformer Decoding, https://arxiv.org/abs/2406.09297

• 13 Jun, An Image is Worth More Than 16x16 Patches: Exploring Transformers on Individual Pixels, https://arxiv.org/abs/2406.09415

• 13 Jun, FouRA: Fourier Low Rank Adaptation, https://arxiv.org/abs/2406.08798

• 14 Jun, Bootstrapping Language Models with DPO Implicit Rewards, https://arxiv.org/abs/2406.09760

• 14 Jun, Be like a Goldfish, Don't Memorize! Mitigating Memorization in Generative LLMs, https://arxiv.org/abs/2406.10209

• 14 Jun, Regularizing Hidden States Enables Learning Generalizable Reward Model for LLMs, https://arxiv.org/abs/2406.10216

• 16 Jun, THEANINE: Revisiting Memory Management in Long-term Conversations with Timeline-augmented Response Generation, https://arxiv.org/abs/2406.10996

• 17 Jun, Task Me Anything, https://arxiv.org/abs/2406.11775

• 17 Jun, How Do Large Language Models Acquire Factual Knowledge During Pretraining?, https://arxiv.org/abs/2406.11813

• 17 Jun, mDPO: Conditional Preference Optimization for Multimodal Large Language Models, https://arxiv.org/abs/2406.11839

• 17 Jun, Nemotron-4 340B Technical Report, https://arxiv.org/abs/2406.11704

• 17 Jun, DataComp-LM: In Search of the Next Generation of Training Sets for Language Models, https://arxiv.org/abs/2406.11794

• 17 Jun, Tokenization Falling Short: The Curse of Tokenization, https://arxiv.org/abs/2406.11687

• 17 Jun, DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence, https://arxiv.org/abs/2406.11931

• 17 Jun, Unveiling Encoder-Free Vision-Language Models, https://arxiv.org/abs/2406.11832

• 17 Jun, Iterative Length-Regularized Direct Preference Optimization: A Case Study on Improving 7B Language Models to GPT-4 Level, https://arxiv.org/abs/2406.11817

• 17 Jun, HARE: HumAn pRiors, a key to small language model Efficiency, https://arxiv.org/abs/2406.11410

• 17 Jun, Measuring memorization in RLHF for code completion, https://arxiv.org/abs/2406.11715

• 17 Jun, Self-MoE: Towards Compositional Large Language Models with Self-Specialized Experts, https://arxiv.org/abs/2406.12034

• 18 Jun, From RAGs to Rich Parameters: Probing How Language Models Utilize External Knowledge Over Parametric Information for Factual Queries, https://arxiv.org/abs/2406.12824

• 18 Jun, Judging the Judges: Evaluating Alignment and Vulnerabilities in LLMs-as-Judges, https://arxiv.org/abs/2406.12624

• 19 Jun, Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?, https://arxiv.org/abs/2406.13121

• 20 Jun, Instruction Pre-Training: Language Models are Supervised Multitask Learners, https://arxiv.org/abs/2406.14491

• 20 Jun, Can LLMs Learn by Teaching? A Preliminary Study, https://arxiv.org/abs/2406.14629

• 21 Jun, A Tale of Trust and Accuracy: Base vs. Instruct LLMs in RAG Systems, https://arxiv.org/abs/2406.14972

• 21 Jun, LongRAG: Enhancing Retrieval-Augmented Generation with Long-context LLMs, https://arxiv.org/abs/2406.15319

• 21 Jun, MoA: Mixture of Sparse Attention for Automatic Large Language Model Compression, https://arxiv.org/abs/2406.14909

• 21 Jun, Efficient Continual Pre-training by Mitigating the Stability Gap, https://arxiv.org/abs/2406.14833

• 24 Jun, Sparser is Faster and Less is More: Efficient Sparse Attention for Long-Range Transformers, https://arxiv.org/abs/2406.16747

• 24 Jun, WARP: On the Benefits of Weight Averaged Rewarded Policies, https://arxiv.org/abs/2406.16768

• 24 Jun, Adam-mini: Use Fewer Learning Rates To Gain More, https://arxiv.org/abs/2406.16793

• 25 Jun, The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale, https://arxiv.org/abs/2406.17557

• 25 Jun, LongIns: A Challenging Long-context Instruction-based Exam for LLMs, https://arxiv.org/abs/2406.17588

• 25 Jun, Following Length Constraints in Instructions, https://arxiv.org/abs/2406.17744

• 26 Jun, A Closer Look into Mixture-of-Experts in Large Language Models, https://arxiv.org/abs/2406.18219

• 26 Jun, RouteLLM: Learning to Route LLMs with Preference Data, https://arxiv.org/abs/2406.18665

• 26 Jun, Step-DPO: Step-wise Preference Optimization for Long-chain Reasoning of LLMs, https://arxiv.org/abs/2406.18629

• 27 Jun, Dataset Size Recovery from LoRA Weights, https://arxiv.org/abs/2406.19395

• 27 Jun, From Artificial Needles to Real Haystacks: Improving Retrieval Capabilities in LLMs by Finetuning on Synthetic Data, https://arxiv.org/abs/2406.19292

• 27 Jun, Changing Answer Order Can Decrease MMLU Accuracy, https://arxiv.org/abs/2406.19470

• 28 Jun, Direct Preference Knowledge Distillation for Large Language Models, https://arxiv.org/abs/2406.19774

• 28 Jun, LLM Critics Help Catch LLM Bugs, https://arxiv.org/abs/2407.00215

• 28 Jun, Scaling Synthetic Data Creation with 1,000,000,000 Personas, https://arxiv.org/abs/2406.20094

• 1 Jul, LLM See, LLM Do: Guiding Data Generation to Target Non-Differentiable Objectives, https://arxiv.org/abs/2407.01490

• 1 Jul, Searching for Best Practices in Retrieval-Augmented Generation, https://arxiv.org/abs/2407.01219

• 1 Jul, Let the Expert Stick to His Last: Expert-Specialized Fine-Tuning for Sparse Architectural Large Language Models, https://arxiv.org/abs/2407.01906

• 1 Jul, Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion, https://arxiv.org/abs/2407.01392

• 1 Jul, Eliminating Position Bias of Language Models: A Mechanistic Approach, https://arxiv.org/abs/2407.01100

• 2 Jul, JMInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention, https://arxiv.org/abs/2407.02490

• 2 Jul, TokenPacker: Efficient Visual Projector for Multimodal LLM, https://arxiv.org/abs/2407.02392

• 2 Jul, Reasoning in Large Language Models: A Geometric Perspective, https://arxiv.org/abs/2407.02678

• 2 Jul, RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs, https://arxiv.org/abs/2407.02485

• 3 Jul, AgentInstruct: Toward Generative Teaching with Agentic Flows, https://arxiv.org/abs/2407.03502

• 3 Jul, HEMM: Holistic Evaluation of Multimodal Foundation Models, https://arxiv.org/abs/2407.03418

• 4 Jul, Mixture of A Million Experts, https://arxiv.org/abs/2407.04153

• 5 Jul, Learning to (Learn at Test Time): RNNs with Expressive Hidden States, https://arxiv.org/abs/2407.04620

• 9 Jul, Vision Language Models Are Blind, https://arxiv.org/abs/2407.06581

• 9 Jul, Self-Recognition in Language Models, https://arxiv.org/abs/2407.06946

• 10 Jul, Inference Performance Optimization for Large Language Models on CPUs, https://arxiv.org/abs/2407.07304

• 11 Jul, Gradient Boosting Reinforcement Learning, https://arxiv.org/abs/2407.08250

• 11 Jul, FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision, https://arxiv.org/abs/2407.08608

• 12 Jul, SpreadsheetLLM: Encoding Spreadsheets for Large Language Models, https://arxiv.org/abs/2407.09025

• 12 Jul, New Desiderata for Direct Preference Optimization, https://arxiv.org/abs/2407.09072

• 12 Jul, Context Embeddings for Efficient Answer Generation in RAG, https://arxiv.org/abs/2407.09252

• 15 Jul, Qwen2 Technical Report, https://arxiv.org/abs/2407.10671

• 15 Jul, The Good, The Bad, and The Greedy: Evaluation of LLMs Should Not Ignore Non-Determinism, https://arxiv.org/abs/2407.10457

• 15 Jul, From GaLore to WeLore: How Low-Rank Weights Non-uniformly Emerge from Low-Rank Gradients, https://arxiv.org/abs/2407.11239

• 16 Jul, GoldFinch: High Performance RWKV/Transformer Hybrid with Linear Pre-Fill and Extreme KV-Cache Compression, https://arxiv.org/abs/2407.12077

• 16 Jul, Scaling Diffusion Transformers to 16 Billion Parameters, https://arxiv.org/abs/2407.11633

• 16 Jul, NeedleBench: Can LLMs Do Retrieval and Reasoning in 1 Million Context Window?, https://arxiv.org/abs/2407.11963

• 17 Jul, Patch-Level Training for Large Language Models, https://arxiv.org/abs/2407.12665

• 17 Jul, LMMs-Eval: Reality Check on the Evaluation of Large Multimodal Models, https://arxiv.org/abs/2407.12772

• 17 Jul, A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks, https://arxiv.org/abs/2407.12994

• 17 Jul, Spectra: A Comprehensive Study of Ternary, Quantized, and FP16 Language Models, https://arxiv.org/abs/2407.12327

• 18 Jul, Attention Overflow: Language Model Input Blur during Long-Context Missing Items Recommendation, https://arxiv.org/abs/2407.13481

• 18 Jul, Weak-to-Strong Reasoning, https://arxiv.org/abs/2407.13647

• 18 Jul, Understanding Reference Policies in Direct Preference Optimization, https://arxiv.org/abs/2407.13709

• 18 Jul, Scaling Laws with Vocabulary: Larger Models Deserve Larger Vocabularies, https://arxiv.org/abs/2407.13623

• 19 Jul, BOND: Aligning LLMs with Best-of-N Distillation, https://arxiv.org/abs/2407.14622

• 19 Jul, Compact Language Models via Pruning and Knowledge Distillation, https://arxiv.org/abs/2407.14679

• 19 Jul, LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference, https://arxiv.org/abs/2407.14057

• 22 Jul, Mini-Sequence Transformer: Optimizing Intermediate Memory for Long Sequences Training, https://arxiv.org/abs/2407.15892

• 22 Jul, DDK: Distilling Domain Knowledge for Efficient Large Language Models, https://arxiv.org/abs/2407.16154

• 23 Jul, Generation Constraint Scaling Can Mitigate Hallucination, https://arxiv.org/abs/2407.16908

• 23 Jul, Retrieval Augmented Generation or Long-Context LLMs? A Comprehensive Study and Hybrid Approach, https://arxiv.org/abs/2407.16833

• 23 Jul, Course-Correction: Safety Alignment Using Synthetic Preferences, https://arxiv.org/abs/2407.16637

• 26 Jul, Data Mixture Inference: What do BPE Tokenizers Reveal about their Training Data?, https://arxiv.org/abs/2407.16607

• 28 Jul, Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-Judge, https://arxiv.org/abs/2407.19594

• 29 Jul, Improving Retrieval Augmented Language Model with Self-Reasoning, https://arxiv.org/abs/2407.19813

• 29 Jul, Apple Intelligence Foundation Language Models, https://arxiv.org/abs/2407.21075

• 30 Jul, ThinK: Thinner Key Cache by Query-Driven Pruning, https://arxiv.org/abs/2407.21018

• 31 Jul, The Llama 3 Herd of Models, https://arxiv.org/abs/2407.21783

• 31 Jul, Gemma 2: Improving Open Language Models at a Practical Size, https://arxiv.org/abs/2408.00118

• 1 Aug, SAM 2: Segment Anything in Images and Videos, https://arxiv.org/abs/2408.00714

• 2 Aug, POA: Pre-training Once for Models of All Sizes, https://arxiv.org/abs/2408.01031

• 2 Aug, RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework, https://arxiv.org/abs/2408.01262

• 2 Aug, A Survey of Mamba, https://arxiv.org/abs/2408.01129

• 3 Aug, MiniCPM-V: A GPT-4V Level MLLM on Your Phone, https://arxiv.org/abs/2408.01800

• 5 Aug, RAG Foundry: A Framework for Enhancing LLMs for Retrieval Augmented Generation, https://arxiv.org/abs/2408.02545

• 5 Aug, Self-Taught Evaluators, https://arxiv.org/abs/2408.02666

• 5 Aug, BioMamba: A Pre-trained Biomedical Language Representation Model Leveraging Mamba, https://arxiv.org/abs/2408.02600

• 5 Aug, Self-Taught Evaluators, https://arxiv.org/abs/2408.02666

• 7 Aug, EXAONE 3.0 7.8B Instruction Tuned Language Model, https://arxiv.org/abs/2408.03541

• 7 Aug, 1.5-Pints Technical Report: Pretraining in Days, Not Months -- Your Language Model Thrives on Quality Data, https://arxiv.org/abs/2408.03506

• 8 Aug, Conversational Prompt Engineering, https://arxiv.org/abs/2408.04560

• 8 Aug, Trans-Tokenization and Cross-lingual Vocabulary Transfers: Language Adaptation of LLMs for Low-Resource NLP, https://arxiv.org/abs/2408.04303

• 12 Aug, The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery, https://arxiv.org/abs/2408.06292

• 15 Aug, Hermes 3 Technical Report, https://arxiv.org/abs/2408.12570

• 19 Aug, Customizing Language Models with Instance-wise LoRA for Sequential Recommendation, https://arxiv.org/abs/2408.10159

• 20 Aug, Enhancing Robustness in Large Language Models: Prompting for Mitigating the Impact of Irrelevant Information, https://arxiv.org/abs/2408.10615

• 20 Aug, To Code, or Not To Code? Exploring Impact of Code in Pre-training, https://arxiv.org/abs/2408.10914

• 21 Aug , LLM Pruning and Distillation in Practice: The Minitron Approach, https://arxiv.org/abs/2408.11796

• 22 Aug, Jamba-1.5: Hybrid Transformer-Mamba Models at Scale, https://arxiv.org/abs/2408.12570

• 22 Aug, Controllable Text Generation for Large Language Models: A Survey, https://arxiv.org/abs/2408.12599

• 23 Aug, Multi-Layer Transformers Gradient Can be Approximated in Almost Linear Time, https://arxiv.org/abs/2408.13233

• 26 Aug, A Practitioner's Guide to Continual Multimodal Pretraining, https://arxiv.org/abs/2408.14471

• 26 Aug, Building and better understanding vision-language models: insights and future directions, https://arxiv.org/abs/2408.12637

• 26 Aug, CURLoRA: Stable LLM Continual Fine-Tuning and Catastrophic Forgetting Mitigation, https://arxiv.org/abs/2408.14572

• 27 Aug, The Mamba in the Llama: Distilling and Accelerating Hybrid Models, https://arxiv.org/abs/2408.15237

• 28 Aug, ReMamba: Equip Mamba with Effective Long-Sequence Modeling, https://arxiv.org/abs/2408.15496

• 29 Aug, Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal Sampling, https://arxiv.org/abs/2408.16737

• 31 Aug, LongRecipe: Recipe for Efficient Long Context Generalization in Large Languge Models, https://arxiv.org/abs/2409.00509

• 3 Sep, OLMoE: Open Mixture-of-Experts Language Models, https://arxiv.org/abs/2409.02060

• 3 Sep 2024, In Defense of RAG in the Era of Long-Context Language Models, https://arxiv.org/abs/2409.01666

• 5 Sep, Attention Heads of Large Language Models: A Survey, https://arxiv.org/abs/2409.03752

• 5 Sep, LongCite: Enabling LLMs to Generate Fine-grained Citations in Long-context QA, https://arxiv.org/abs/2409.02897

• 5 Sep, How Do Your Code LLMs Perform? Empowering Code Instruction Tuning with High-Quality Data, https://arxiv.org/abs/2409.03810

• 6 Sep, Theory, Analysis, and Best Practices for Sigmoid Self-Attention, https://arxiv.org/abs/2409.04431

• 10 Sep, LLaMA-Omni: Seamless Speech Interaction with Large Language Models, https://arxiv.org/abs/2409.06666

• 10 Sep, What is the Role of Small Models in the LLM Era: A Survey, https://arxiv.org/abs/2409.06857

• 11 Sep, Policy Filtration in RLHF to Fine-Tune LLM for Code Generation, https://arxiv.org/abs/2409.06957

• 16 Sep, RetrievalAttention: Accelerating Long-Context LLM Inference via Vector Retrieval, https://arxiv.org/abs/2409.10516

• 18 Sep, Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement, https://arxiv.org/abs/2409.12122

• 18 Sep, Qwen2.5-Coder Technical Report, https://arxiv.org/abs/2409.12186

• 21 Sep, Instruction Following without Instruction Tuning, https://arxiv.org/abs/2409.14254

• 30 Sep, Is Preference Alignment Always the Best Option to Enhance LLM-Based Translation? An Empirical Analysis, https://arxiv.org/abs/2409.20059

• 30 Sep, The Perfect Blend: Redefining RLHF with Mixture of Judges, https://arxiv.org/abs/2409.20370 (New paper by Meta on how they did RLHF for Llama 3)

• 1 Oct, Addition is All You Need for Energy-efficient Language Models, https://arxiv.org/abs/2410.00907

• 2 Oct Quantifying Generalization Complexity for Large Language Models, https://arxiv.org/abs/2410.01769

• 2 Oct, When a language model is optimized for reasoning, does it still show embers of autoregression? An analysis of OpenAI o1, https://arxiv.org/abs/2410.01792

• 2 Oct, Were RNNs All We Needed?, https://arxiv.org/abs/2410.01201

• 3 Oct, Selective Attention Improves Transformer, https://arxiv.org/abs/2410.02703

• 3 Oct, LLMs Know More Than They Show: On the Intrinsic Representation of LLM Hallucinations, https://arxiv.org/abs/2410.02707

• 3 Oct, LLaVA-Critic: Learning to Evaluate Multimodal Models, https://arxiv.org/abs/2410.02712

• 7 Oct, Differential Transformer, https://arxiv.org/abs/2410.05258

• 7 Oct, GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models, https://arxiv.org/abs/2410.05229

• 8 Oct, ARIA: An Open Multimodal Native Mixture-of-Experts Model, https://arxiv.org/abs/2410.05993

• 8 Oct, O1 Replication Journey: A Strategic Progress Report -- Part 1, https://arxiv.org/abs/2410.18982

• 8 Oct, Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAG, https://arxiv.org/abs/2410.05983

• 9 Oct, From Generalist to Specialist: Adapting Vision Language Models via Task-Specific Visual Instruction Tuning, https://arxiv.org/abs/2410.06456

• 10 Oct, KV Prediction for Improved Time to First Token, https://arxiv.org/abs/2410.08391

• 11 Oct, Baichuan-Omni Technical Report, https://arxiv.org/abs/2410.08565

• 13 Oct, MMIE: Massive Multimodal Interleaved Comprehension Benchmark for Large Vision-Language Models, https://arxiv.org/abs/2410.10139

• 13 Oct, LOKI: A Comprehensive Synthetic Data Detection Benchmark using Large Multimodal Models, https://arxiv.org/abs/2410.09732

• 15 Oct, AFlow: Automating Agentic Workflow Generation, https://arxiv.org/abs/2410.10762

• 15 Oct, Toward General Instruction-Following Alignment for Retrieval-Augmented Generation, https://arxiv.org/abs/2410.09584

• 21 Oct, Pre-training Distillation for Large Language Models: A Design Space Exploration, https://arxiv.org/abs/2410.16215

• 23 Oct, MIA-DPO: Multi-Image Augmented Direct Preference Optimization For Large Vision-Language Models, https://arxiv.org/abs/2410.17637

• 23 Oct, Scalable Ranked Preference Optimization for Text-to-Image Generation, https://arxiv.org/abs/2410.18013

• 23 Oct, Scaling Diffusion Language Models via Adaptation from Autoregressive Models, https://arxiv.org/abs/2410.17891

• 24 Oct, Hybrid Preferences: Learning to Route Instances for Human vs. AI Feedback, https://arxiv.org/abs/2410.19133

• 25 Oct, Counting Ability of Large Language Models and Impact of Tokenization, https://arxiv.org/abs/2410.19730

• 25 Oct, A Survey of Small Language Models, https://arxiv.org/abs/2410.20011

• 26 Oct, Accelerating Direct Preference Optimization with Prefix Sharing, https://arxiv.org/abs/2410.20305

• 27 Oct, Mind Your Step (by Step): Chain-of-Thought can Reduce Performance on Tasks where Thinking Makes Humans Worse, https://arxiv.org/abs/2410.21333

• 28 Oct, LongReward: Improving Long-context Large Language Models with AI Feedback, https://arxiv.org/abs/2410.21252

• 28 Oct, ShadowKV: KV Cache in Shadows for High-Throughput Long-Context LLM Inference, https://arxiv.org/abs/2410.21465

• 29 Oct, Beyond Text: Optimizing RAG with Multimodal Inputs for Industrial Applications, https://arxiv.org/abs/2410.21943

• 30 Oct, CORAL: Benchmarking Multi-turn Conversational Retrieval-Augmentation Generation, https://arxiv.org/abs/2410.23090

• 31 Oct, What Happened in LLMs Layers when Trained for Fast vs. Slow Thinking: A Gradient Perspective, https://arxiv.org/abs/2410.23743

• 31 Oct, GPT or BERT: why not both?, https://arxiv.org/abs/2410.24159

• 31 Oct, Language Models can Self-Lengthen to Generate Long Texts, https://arxiv.org/abs/2410.23933

• 1 Nov, Adding Error Bars to Evals: A Statistical Approach to Language Model Evaluations, https://arxiv.org/abs/2411.00640

• 1 Nov 2024, Adapting While Learning: Grounding LLMs for Scientific Problems with Intelligent Tool Usage Adaptation, https://arxiv.org/abs/2411.00412

• 1 Nov 2024, Multi-expert Prompting Improves Reliability, Safety, and Usefulness of Large Language Models, https://arxiv.org/abs/2411.00492

• 3 Nov, Sample-Efficient Alignment for LLMs, https://arxiv.org/abs/2411.01493

• 4 Nov 2024, A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness, https://arxiv.org/abs/2411.03350

• 4 Nov, "Give Me BF16 or Give Me Death"? Accuracy-Performance Trade-Offs in LLM Quantization, https://arxiv.org/abs/2411.02355

• 4 Nov, Parameter-Efficient Fine-Tuning of Large Language Models for Unit Test Generation: An Empirical Study, https://arxiv.org/abs/2411.02462

• 5 Nov, HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems, https://arxiv.org/abs/2411.02959

• 6 Nov, Both Text and Images Leaked! A Systematic Analysis of Multimodal LLM Data Contamination, https://arxiv.org/abs/2411.03823

• 6 Nov, Language Models are Hidden Reasoners: Unlocking Latent Reasoning Capabilities via Self-Rewarding, https://arxiv.org/abs/2411.04282

• 6 Nov, Number Cookbook: Number Understanding of Language Models and How to Improve It, https://arxiv.org/abs/2411.03766

• 7 Nov, Mixture-of-Transformers: A Sparse and Scalable Architecture for Multi-Modal Foundation Models, https://arxiv.org/abs/2411.04996

• 7 Nov, BitNet a4.8: 4-bit Activations for 1-bit LLMs, https://arxiv.org/abs/2411.04965

• 7 Nov, Scaling Laws for Precision, https://arxiv.org/abs/2411.04330

• 8 Nov, Energy Efficient Protein Language Models: Leveraging Small Language Models with LoRA for Controllable Protein Generation, https://arxiv.org/abs/2411.05966

• 8 Nov, Balancing Pipeline Parallelism with Vocabulary Parallelism, https://arxiv.org/abs/2411.05288

• 11 Nov, Toward Optimal Search and Retrieval for RAG, https://arxiv.org/abs/2411.07396

• 12 Nov, Large Language Models Can Self-Improve in Long-context Reasoning, https://arxiv.org/abs/2411.08147

• 12 Nov, Stronger Models are NOT Stronger Teachers for Instruction Tuning, https://arxiv.org/abs/2411.07133

• 12 Nov, Direct Preference Optimization Using Sparse Feature-Level Constraints, https://arxiv.org/abs/2411.07618

• 13 Nov, Cut Your Losses in Large-Vocabulary Language Models, https://arxiv.org/abs/2411.09009

• 15 Nov, Does Prompt Formatting Have Any Impact on LLM Performance?, https://arxiv.org/abs/2411.10541

• 17 Nov, SymDPO: Boosting In-Context Learning of Large Multimodal Models with Symbol Demonstration Direct Preference Optimization, https://arxiv.org/abs/2411.11909

• 17 Nov, SageAttention2 Technical Report: Accurate 4 Bit Attention for Plug-and-play Inference Acceleration, https://arxiv.org/abs/2411.10958

• 18 Nov, Bi-Mamba: Towards Accurate 1-Bit State Space Models, https://arxiv.org/abs/2411.11843

• 19 Nov, RedPajama: an Open Dataset for Training Large Language Models, https://arxiv.org/abs/2411.12372

• 20 Nov, Hymba: A Hybrid-head Architecture for Small Language Models, https://arxiv.org/abs/2411.13676

• 20 Nov, Loss-to-Loss Prediction: Scaling Laws for All Datasets, https://arxiv.org/abs/2411.12925

• 21 Nov, When Precision Meets Position: BFloat16 Breaks Down RoPE in Long-Context Training, https://arxiv.org/abs/2411.13476

• 21 Nov, Multimodal Autoregressive Pre-training of Large Vision Encoders, https://arxiv.org/abs/2411.14402

• 21 Nov, Natural Language Reinforcement Learning, https://arxiv.org/abs/2411.14251

• 22 Nov, Large Multi-modal Models Can Interpret Features in Large Multi-modal Models, https://arxiv.org/abs/2411.14982

• 23 Nov, MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs, https://arxiv.org/abs/2411.15296

• 23 Nov, TÜLU 3: Pushing Frontiers in Open Language Model Post-Training, https://arxiv.org/abs/2411.15124

• 24 Nov, LLMs Do Not Think Step-by-step In Implicit Reasoning, https://arxiv.org/abs/2411.15862

• In progress…

In progress…

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