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Aligning Teacher with Student Preferences for Tailored Training Data Generation
Paper • 2406.19227 • Published • 24 -
Pre-training Distillation for Large Language Models: A Design Space Exploration
Paper • 2410.16215 • Published • 15 -
Baichuan Alignment Technical Report
Paper • 2410.14940 • Published • 48 -
MiniPLM: Knowledge Distillation for Pre-Training Language Models
Paper • 2410.17215 • Published • 12
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Collections including paper arxiv:2406.19227
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Internal Consistency and Self-Feedback in Large Language Models: A Survey
Paper • 2407.14507 • Published • 46 -
New Desiderata for Direct Preference Optimization
Paper • 2407.09072 • Published • 9 -
Self-Recognition in Language Models
Paper • 2407.06946 • Published • 24 -
MJ-Bench: Is Your Multimodal Reward Model Really a Good Judge for Text-to-Image Generation?
Paper • 2407.04842 • Published • 52
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Chain-of-Knowledge: Integrating Knowledge Reasoning into Large Language Models by Learning from Knowledge Graphs
Paper • 2407.00653 • Published • 11 -
Token Erasure as a Footprint of Implicit Vocabulary Items in LLMs
Paper • 2406.20086 • Published • 4 -
UnUnlearning: Unlearning is not sufficient for content regulation in advanced generative AI
Paper • 2407.00106 • Published • 5 -
MIRAI: Evaluating LLM Agents for Event Forecasting
Paper • 2407.01231 • Published • 16
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Learn Your Reference Model for Real Good Alignment
Paper • 2404.09656 • Published • 82 -
Aligning Teacher with Student Preferences for Tailored Training Data Generation
Paper • 2406.19227 • Published • 24 -
Self-Play Preference Optimization for Language Model Alignment
Paper • 2405.00675 • Published • 24 -
CantTalkAboutThis: Aligning Language Models to Stay on Topic in Dialogues
Paper • 2404.03820 • Published • 24
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Large Language Model Unlearning via Embedding-Corrupted Prompts
Paper • 2406.07933 • Published • 7 -
Block Transformer: Global-to-Local Language Modeling for Fast Inference
Paper • 2406.02657 • Published • 37 -
Learn Beyond The Answer: Training Language Models with Reflection for Mathematical Reasoning
Paper • 2406.12050 • Published • 18 -
How Do Large Language Models Acquire Factual Knowledge During Pretraining?
Paper • 2406.11813 • Published • 30
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MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs
Paper • 2402.15627 • Published • 34 -
Beyond Language Models: Byte Models are Digital World Simulators
Paper • 2402.19155 • Published • 49 -
VisionLLaMA: A Unified LLaMA Interface for Vision Tasks
Paper • 2403.00522 • Published • 44 -
Stealing Part of a Production Language Model
Paper • 2403.06634 • Published • 90
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Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 21 -
OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 80 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 144 -
SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity
Paper • 2401.17072 • Published • 25
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Suppressing Pink Elephants with Direct Principle Feedback
Paper • 2402.07896 • Published • 9 -
Policy Improvement using Language Feedback Models
Paper • 2402.07876 • Published • 5 -
Direct Language Model Alignment from Online AI Feedback
Paper • 2402.04792 • Published • 29 -
Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models
Paper • 2401.01335 • Published • 64