The Goldilocks of Pragmatic Understanding: Fine-Tuning Strategy Matters for Implicature Resolution by LLMs
Paper • 2210.14986 • Published • 5
— Journal Club 📚 —
Candidate papers to read in the Hugging Face journal club
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Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2
Paper • 2311.10702 • Published • 19 -
Large Language Models as Optimizers
Paper • 2309.03409 • Published • 79 -
From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting
Paper • 2309.04269 • Published • 34 -
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models
Paper • 2312.06585 • Published • 29 -
Mathematical Language Models: A Survey
Paper • 2312.07622 • Published • 2 -
TinyGSM: achieving >80% on GSM8k with small language models
Paper • 2312.09241 • Published • 40 -
Helping or Herding? Reward Model Ensembles Mitigate but do not Eliminate Reward Hacking
Paper • 2312.09244 • Published • 9 -
ReST meets ReAct: Self-Improvement for Multi-Step Reasoning LLM Agent
Paper • 2312.10003 • Published • 44 -
How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition
Paper • 2310.05492 • Published • 2 -
Data Diversity Matters for Robust Instruction Tuning
Paper • 2311.14736 • Published • 2 -
AlpaGasus: Training A Better Alpaca with Fewer Data
Paper • 2307.08701 • Published • 24 -
WaveCoder: Widespread And Versatile Enhanced Instruction Tuning with Refined Data Generation
Paper • 2312.14187 • Published • 50 -
Principled Instructions Are All You Need for Questioning LLaMA-1/2, GPT-3.5/4
Paper • 2312.16171 • Published • 37 -
What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning
Paper • 2312.15685 • Published • 17 -
Some things are more CRINGE than others: Preference Optimization with the Pairwise Cringe Loss
Paper • 2312.16682 • Published • 5 -
Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations
Paper • 2312.08935 • Published • 4 -
Let's Verify Step by Step
Paper • 2305.20050 • Published • 11 -
R-Tuning: Teaching Large Language Models to Refuse Unknown Questions
Paper • 2311.09677 • Published • 3 -
Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models
Paper • 2401.01335 • Published • 69 -
A Minimaximalist Approach to Reinforcement Learning from Human Feedback
Paper • 2401.04056 • Published • 2 -
Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training
Paper • 2401.05566 • Published • 30 -
Secrets of RLHF in Large Language Models Part II: Reward Modeling
Paper • 2401.06080 • Published • 27 -
LLMs cannot find reasoning errors, but can correct them!
Paper • 2311.08516 • Published • 1 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 156 -
WARM: On the Benefits of Weight Averaged Reward Models
Paper • 2401.12187 • Published • 19 -
ReFT: Reasoning with Reinforced Fine-Tuning
Paper • 2401.08967 • Published • 32 -
Long Is More for Alignment: A Simple but Tough-to-Beat Baseline for Instruction Fine-Tuning
Paper • 2402.04833 • Published • 5 -
V-STaR: Training Verifiers for Self-Taught Reasoners
Paper • 2402.06457 • Published • 9 -
Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint
Paper • 2312.11456 • Published • 1 -
ICDPO: Effectively Borrowing Alignment Capability of Others via In-context Direct Preference Optimization
Paper • 2402.09320 • Published • 6 -
Chain-of-Thought Reasoning Without Prompting
Paper • 2402.10200 • Published • 111 -
The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learning
Paper • 2312.01552 • Published • 31 -
DsDm: Model-Aware Dataset Selection with Datamodels
Paper • 2401.12926 • Published • 2 -
A Critical Evaluation of AI Feedback for Aligning Large Language Models
Paper • 2402.12366 • Published • 3 -
Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs
Paper • 2402.14740 • Published • 18 -
Functional Benchmarks for Robust Evaluation of Reasoning Performance, and the Reasoning Gap
Paper • 2402.19450 • Published • 3 -
ArCHer: Training Language Model Agents via Hierarchical Multi-Turn RL
Paper • 2402.19446 • Published -
When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method
Paper • 2402.17193 • Published • 27 -
Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation
Paper • 2402.18334 • Published • 12 -
GSM-Plus: A Comprehensive Benchmark for Evaluating the Robustness of LLMs as Mathematical Problem Solvers
Paper • 2402.19255 • Published • 1 -
Aligning Large Language Models by On-Policy Self-Judgment
Paper • 2402.11253 • Published • 2 -
Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM
Paper • 2403.07816 • Published • 45 -
Teaching Large Language Models to Reason with Reinforcement Learning
Paper • 2403.04642 • Published • 48 -
Simple and Scalable Strategies to Continually Pre-train Large Language Models
Paper • 2403.08763 • Published • 52 -
MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training
Paper • 2403.09611 • Published • 130 -
InternLM2 Technical Report
Paper • 2403.17297 • Published • 35 -
Advancing LLM Reasoning Generalists with Preference Trees
Paper • 2404.02078 • Published • 46 -
More Agents Is All You Need
Paper • 2402.05120 • Published • 59 -
Rho-1: Not All Tokens Are What You Need
Paper • 2404.07965 • Published • 95 -
From r to Q^*: Your Language Model is Secretly a Q-Function
Paper • 2404.12358 • Published • 2 -
Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study
Paper • 2404.10719 • Published • 6 -
Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking
Paper • 2403.09629 • Published • 80 -
Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing
Paper • 2404.12253 • Published • 55 -
Explorative Modeling: Unlocking a Third Pretraining Axis and End-to-End Generation
Paper • 2607.27372 • Published • 13