1 citations · 1 across the 6 of their papers we have counts for
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Align Documents to Questions: Question-Oriented Document Rewriting for Retrieval-Augmented Generation
Jiaang Li, Zhendong Mao, Quan Wang +2
Retrieval-Augmented Generation (RAG) enhances the factuality of Large Language Models (LLMs) by incorporating retrieved documents and/or generated context. However, LLMs often exhi…
In-Token Rationality Optimization: Towards Accurate and Concise LLM Reasoning via Self-Feedback
Mingye Zhu, Yi Liu, Zheren Fu +2
Training Large Language Models (LLMs) for chain-of-thought reasoning presents a significant challenge: supervised fine-tuning on a single "golden" rationale hurts generalization as…
Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability
Chiwei Zhu, Benfeng Xu, An Yang +4
Training language models with rationales augmentation has been shown to be beneficial in many existing works. In this paper, we identify that such a prevailing view does not hold c…
Leveraging Robust Optimization for LLM Alignment under Distribution Shifts
Mingye Zhu, Yi Liu, Zheren Fu +2
Preference alignment methods are increasingly critical for steering large language models (LLMs) to generate outputs consistent with human values. While recent approaches often rel…
FlipGuard: Defending Preference Alignment against Update Regression with Constrained Optimization
Mingye Zhu, Yi Liu, Quan Wang +2
Recent breakthroughs in preference alignment have significantly improved Large Language Models' ability to generate texts that align with human preferences and values. However, cur…
E-CORE: Emotion Correlation Enhanced Empathetic Dialogue Generation
Fengyi Fu, Lei Zhang, Quan Wang +1
Achieving empathy is a crucial step toward humanized dialogue systems. Current approaches for empathetic dialogue generation mainly perceive an emotional label to generate an empat…