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Collections including paper arxiv:2405.01525

AI Paper of the Day

A collection of papers that I think are interesting, one added each day

Can Large Language Models Understand Context?

Paper • 2402.00858 • Published Feb 1 • 21
OLMo: Accelerating the Science of Language Models

Paper • 2402.00838 • Published Feb 1 • 79
Self-Rewarding Language Models

Paper • 2401.10020 • Published Jan 18 • 143
SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity

Paper • 2401.17072 • Published Jan 30 • 25

Moral Foundations of Large Language Models

Paper • 2310.15337 • Published Oct 23, 2023 • 1
Specific versus General Principles for Constitutional AI

Paper • 2310.13798 • Published Oct 20, 2023 • 2
Contrastive Prefence Learning: Learning from Human Feedback without RL

Paper • 2310.13639 • Published Oct 20, 2023 • 24
RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback

Paper • 2309.00267 • Published Sep 1, 2023 • 47

FLAME: Factuality-Aware Alignment for Large Language Models

Paper • 2405.01525 • Published May 2 • 24

FLAME: Factuality-Aware Alignment for Large Language Models

Paper • 2405.01525 • Published May 2 • 24
DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data

Paper • 2405.14333 • Published May 23 • 34
Transformers Can Do Arithmetic with the Right Embeddings

Paper • 2405.17399 • Published May 27 • 51
EasyAnimate: A High-Performance Long Video Generation Method based on Transformer Architecture

Paper • 2405.18991 • Published May 29 • 12

Scientific Intelligence Academy (SIA)

FLAME: Factuality-Aware Alignment for Large Language Models

Paper • 2405.01525 • Published May 2 • 24

LLM Research Papers

FLAME: Factuality-Aware Alignment for Large Language Models

Paper • 2405.01525 • Published May 2 • 24

FLAME: Factuality-Aware Alignment for Large Language Models

Paper • 2405.01525 • Published May 2 • 24
Training Language Models to Self-Correct via Reinforcement Learning

Paper • 2409.12917 • Published Sep 19 • 133

Papers - Fine-tuning - DPO

Refer to additional papers: https://link.springer.com/article/10.1007/s10994-014-5458-8 and https://link.springer.com/article/10.1007/BF00992696

Direct Preference Optimization: Your Language Model is Secretly a Reward Model

Paper • 2305.18290 • Published May 29, 2023 • 47
ICDPO: Effectively Borrowing Alignment Capability of Others via In-context Direct Preference Optimization

Paper • 2402.09320 • Published Feb 14 • 6
sDPO: Don't Use Your Data All at Once

Paper • 2403.19270 • Published Mar 28 • 39
Dueling RL: Reinforcement Learning with Trajectory Preferences

Paper • 2111.04850 • Published Nov 8, 2021 • 2

Interesting things.

AtP*: An efficient and scalable method for localizing LLM behaviour to components

Paper • 2403.00745 • Published Mar 1 • 11
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Paper • 2402.17764 • Published Feb 27 • 602
MobiLlama: Towards Accurate and Lightweight Fully Transparent GPT

Paper • 2402.16840 • Published Feb 26 • 23
LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens

Paper • 2402.13753 • Published Feb 21 • 111

daily_paper_coll

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

Paper • 2402.19427 • Published Feb 29 • 52
Beyond Language Models: Byte Models are Digital World Simulators

Paper • 2402.19155 • Published Feb 29 • 49
StarCoder 2 and The Stack v2: The Next Generation

Paper • 2402.19173 • Published Feb 29 • 134
Simple linear attention language models balance the recall-throughput tradeoff

Paper • 2402.18668 • Published Feb 28 • 18

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