5 papers · 1 filter
An Interactive Paradigm for Deep Research
Lin Ai, Victor S. Bursztyn, Xiang Chen +2
Recent advances in large language models (LLMs) have enabled deep research systems that synthesize comprehensive, report-style answers to open-ended queries by combining retrieval,…
Mitigating Forgetting Between Supervised and Reinforcement Learning Yields Stronger Reasoners
Xiangchi Yuan, Xiang Chen, Tong Yu +4
Large Language Models (LLMs) show strong reasoning abilities, often amplified by Chain-of-Thought (CoT) prompting and reinforcement learning (RL). Although RL algorithms can substa…
A Penalty Goes a Long Way: Measuring Lexical Diversity in Synthetic Texts Under Prompt-Influenced Length Variations
Vijeta Deshpande, Ishita Dasgupta, Uttaran Bhattacharya +3
Synthetic text generated by Large Language Models (LLMs) is increasingly used for further training and improvement of LLMs. Diversity is crucial for the effectiveness of synthetic…
CodeLutra: Boosting LLM Code Generation via Preference-Guided Refinement
Leitian Tao, Xiang Chen, Tong Yu +4
Large Language Models (LLMs) have revolutionized code generation but require significant resources and often over-generalize, limiting their task-specific efficiency. Fine-tuning s…
Detecting Ambiguities to Guide Query Rewrite for Robust Conversations in Enterprise AI Assistants
Md Mehrab Tanjim, Xiang Chen, Victor S. Bursztyn +8
Multi-turn conversations with an Enterprise AI Assistant can be challenging due to conversational dependencies in questions, leading to ambiguities and errors. To address this, we…