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Collections including paper arxiv:2403.19270

sDPO: Don't Use Your Data All at Once

Paper • 2403.19270 • Published Mar 28, 2024 • 41

LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression

Paper • 2403.12968 • Published Mar 19, 2024 • 25
PERL: Parameter Efficient Reinforcement Learning from Human Feedback

Paper • 2403.10704 • Published Mar 15, 2024 • 58
Alignment Studio: Aligning Large Language Models to Particular Contextual Regulations

Paper • 2403.09704 • Published Mar 8, 2024 • 31
RAFT: Adapting Language Model to Domain Specific RAG

Paper • 2403.10131 • Published Mar 15, 2024 • 69

Alignment-DPO-line

sDPO: Don't Use Your Data All at Once

Paper • 2403.19270 • Published Mar 28, 2024 • 41
Advancing LLM Reasoning Generalists with Preference Trees

Paper • 2404.02078 • Published Apr 2, 2024 • 44
Learn Your Reference Model for Real Good Alignment

Paper • 2404.09656 • Published Apr 15, 2024 • 83
mDPO: Conditional Preference Optimization for Multimodal Large Language Models

Paper • 2406.11839 • Published Jun 17, 2024 • 38

Jamba: A Hybrid Transformer-Mamba Language Model

Paper • 2403.19887 • Published Mar 28, 2024 • 107
sDPO: Don't Use Your Data All at Once

Paper • 2403.19270 • Published Mar 28, 2024 • 41
ViTAR: Vision Transformer with Any Resolution

Paper • 2403.18361 • Published Mar 27, 2024 • 54
Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models

Paper • 2403.18814 • Published Mar 27, 2024 • 47

On the Societal Impact of Open Foundation Models

Paper • 2403.07918 • Published Feb 27, 2024 • 17
sDPO: Don't Use Your Data All at Once

Paper • 2403.19270 • Published Mar 28, 2024 • 41
Hallucinations or Attention Misdirection? The Path to Strategic Value Extraction in Business Using Large Language Models

Paper • 2402.14002 • Published Feb 21, 2024
Evaluating the Social Impact of Generative AI Systems in Systems and Society

Paper • 2306.05949 • Published Jun 9, 2023 • 9

Adapting Large Language Models via Reading Comprehension

Paper • 2309.09530 • Published Sep 18, 2023 • 77
sDPO: Don't Use Your Data All at Once

Paper • 2403.19270 • Published Mar 28, 2024 • 41

Papers - Model - SFT - Alpaca and DPO - Solar

sDPO: Don't Use Your Data All at Once

Paper • 2403.19270 • Published Mar 28, 2024 • 41

Papers - Fine-tuning - SFT

InternLM2 Technical Report

Paper • 2403.17297 • Published Mar 26, 2024 • 31
sDPO: Don't Use Your Data All at Once

Paper • 2403.19270 • Published Mar 28, 2024 • 41
Learn Your Reference Model for Real Good Alignment

Paper • 2404.09656 • Published Apr 15, 2024 • 83
OpenBezoar: Small, Cost-Effective and Open Models Trained on Mixes of Instruction Data

Paper • 2404.12195 • Published Apr 18, 2024 • 12

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 • 53
ICDPO: Effectively Borrowing Alignment Capability of Others via In-context Direct Preference Optimization

Paper • 2402.09320 • Published Feb 14, 2024 • 6
sDPO: Don't Use Your Data All at Once

Paper • 2403.19270 • Published Mar 28, 2024 • 41
Dueling RL: Reinforcement Learning with Trajectory Preferences

Paper • 2111.04850 • Published Nov 8, 2021 • 2

ORPO: Monolithic Preference Optimization without Reference Model

Paper • 2403.07691 • Published Mar 12, 2024 • 64
sDPO: Don't Use Your Data All at Once

Paper • 2403.19270 • Published Mar 28, 2024 • 41
Teaching Large Language Models to Reason with Reinforcement Learning

Paper • 2403.04642 • Published Mar 7, 2024 • 46
Best Practices and Lessons Learned on Synthetic Data for Language Models

Paper • 2404.07503 • Published Apr 11, 2024 • 30

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