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Collections including paper arxiv:2401.08967

Can Large Language Models Understand Context?

Paper • 2402.00858 • Published Feb 1 • 20
Efficient Tool Use with Chain-of-Abstraction Reasoning

Paper • 2401.17464 • Published Jan 30 • 15
ReFT: Reasoning with Reinforced Fine-Tuning

Paper • 2401.08967 • Published Jan 17 • 27
The Impact of Reasoning Step Length on Large Language Models

Paper • 2401.04925 • Published Jan 10 • 15

Candidate papers to read in the H4 journal club

The Goldilocks of Pragmatic Understanding: Fine-Tuning Strategy Matters for Implicature Resolution by LLMs

Paper • 2210.14986 • Published Oct 26, 2022 • 4
Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2

Paper • 2311.10702 • Published Nov 17, 2023 • 17
Large Language Models as Optimizers

Paper • 2309.03409 • Published Sep 7, 2023 • 72
From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting

Paper • 2309.04269 • Published Sep 8, 2023 • 28

Large Language Model Alignment: A Survey

Paper • 2309.15025 • Published Sep 26, 2023 • 2
Aligning Large Language Models with Human: A Survey

Paper • 2307.12966 • Published Jul 24, 2023 • 1
Direct Preference Optimization: Your Language Model is Secretly a Reward Model

Paper • 2305.18290 • Published May 29, 2023 • 37
SteerLM: Attribute Conditioned SFT as an (User-Steerable) Alternative to RLHF

Paper • 2310.05344 • Published Oct 9, 2023 • 1

Chain-of-Thought Reasoning Without Prompting

Paper • 2402.10200 • Published Feb 15 • 91
How to Train Data-Efficient LLMs

Paper • 2402.09668 • Published Feb 15 • 34
BitDelta: Your Fine-Tune May Only Be Worth One Bit

Paper • 2402.10193 • Published Feb 15 • 17
A Human-Inspired Reading Agent with Gist Memory of Very Long Contexts

Paper • 2402.09727 • Published Feb 15 • 35

MambaByte: Token-free Selective State Space Model

Paper • 2401.13660 • Published Jan 24 • 47
Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads

Paper • 2401.10774 • Published Jan 19 • 50
Self-Rewarding Language Models

Paper • 2401.10020 • Published Jan 18 • 135
Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding

Paper • 2401.12954 • Published Jan 23 • 28

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Paper • 2402.19427 • Published Feb 29 • 50
Simple linear attention language models balance the recall-throughput tradeoff

Paper • 2402.18668 • Published Feb 28 • 17
ChunkAttention: Efficient Self-Attention with Prefix-Aware KV Cache and Two-Phase Partition

Paper • 2402.15220 • Published Feb 23 • 18
Linear Transformers are Versatile In-Context Learners

Paper • 2402.14180 • Published Feb 21 • 5

Self-Rewarding Language Models

Paper • 2401.10020 • Published Jan 18 • 135
Tuning Language Models by Proxy

Paper • 2401.08565 • Published Jan 16 • 19
ReFT: Reasoning with Reinforced Fine-Tuning

Paper • 2401.08967 • Published Jan 17 • 27
Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modeling

Paper • 2401.16380 • Published Jan 29 • 46

Self-Rewarding Language Models

Paper • 2401.10020 • Published Jan 18 • 135
ReFT: Reasoning with Reinforced Fine-Tuning

Paper • 2401.08967 • Published Jan 17 • 27
Tuning Language Models by Proxy

Paper • 2401.08565 • Published Jan 16 • 19
TrustLLM: Trustworthiness in Large Language Models

Paper • 2401.05561 • Published Jan 10 • 62

ReFT: Reasoning with Reinforced Fine-Tuning

Paper • 2401.08967 • Published Jan 17 • 27

Chain of Code: Reasoning with a Language Model-Augmented Code Emulator

Paper • 2312.04474 • Published Dec 7, 2023 • 28
Boosting LLM Reasoning: Push the Limits of Few-shot Learning with Reinforced In-Context Pruning

Paper • 2312.08901 • Published Dec 14, 2023
Learning From Mistakes Makes LLM Better Reasoner

Paper • 2310.20689 • Published Oct 31, 2023 • 24
Making Large Language Models Better Reasoners with Step-Aware Verifier

Paper • 2206.02336 • Published Jun 6, 2022 • 1

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