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Condor: Enhance LLM Alignment with Knowledge-Driven Data Synthesis and Refinement
Paper • 2501.12273 • Published • 14 -
CritiQ: Mining Data Quality Criteria from Human Preferences
Paper • 2502.19279 • Published • 11 -
Instruction Pre-Training: Language Models are Supervised Multitask Learners
Paper • 2406.14491 • Published • 96
Eric NG
Eric108
AI & ML interests
NLP
Organizations
None yet
LLMReasoning
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Large Language Models Can Self-Improve in Long-context Reasoning
Paper • 2411.08147 • Published • 65 -
Search, Verify and Feedback: Towards Next Generation Post-training Paradigm of Foundation Models via Verifier Engineering
Paper • 2411.11504 • Published • 24 -
Auto-Evolve: Enhancing Large Language Model's Performance via Self-Reasoning Framework
Paper • 2410.06328 • Published • 2 -
Critical Tokens Matter: Token-Level Contrastive Estimation Enhence LLM's Reasoning Capability
Paper • 2411.19943 • Published • 62
dataSynthesize
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Condor: Enhance LLM Alignment with Knowledge-Driven Data Synthesis and Refinement
Paper • 2501.12273 • Published • 14 -
CritiQ: Mining Data Quality Criteria from Human Preferences
Paper • 2502.19279 • Published • 11 -
Instruction Pre-Training: Language Models are Supervised Multitask Learners
Paper • 2406.14491 • Published • 96
LLMReasoning
-
Large Language Models Can Self-Improve in Long-context Reasoning
Paper • 2411.08147 • Published • 65 -
Search, Verify and Feedback: Towards Next Generation Post-training Paradigm of Foundation Models via Verifier Engineering
Paper • 2411.11504 • Published • 24 -
Auto-Evolve: Enhancing Large Language Model's Performance via Self-Reasoning Framework
Paper • 2410.06328 • Published • 2 -
Critical Tokens Matter: Token-Level Contrastive Estimation Enhence LLM's Reasoning Capability
Paper • 2411.19943 • Published • 62