13 citations · 41 across the 13 of their papers we have counts for
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Only Say What You Know: Calibration-Aware Generation for Long-Form Factuality
Wen Luo, Guangyue Peng, Liang Wang +7
Large Reasoning Models achieve strong performance on complex tasks but remain prone to hallucinations, particularly in long-form generation where errors compound across reasoning s…
Two Pathways to Truthfulness: On the Intrinsic Encoding of LLM Hallucinations
Wen Luo, Guangyue Peng, Wei Li +8
Despite their impressive capabilities, large language models (LLMs) frequently generate hallucinations. Previous work shows that their internal states encode rich signals of truthf…
Well Begun is Half Done: Low-resource Preference Alignment by Weak-to-Strong Decoding
Feifan Song, Shaohang Wei, Wen Luo +4
Large Language Models (LLMs) require alignment with human preferences to avoid generating offensive, false, or meaningless content. Recently, low-resource methods for LLM alignment…
Odysseus Navigates the Sirens' Song: Dynamic Focus Decoding for Factual and Diverse Open-Ended Text Generation
Wen Luo, Feifan Song, Wei Li +3
Large Language Models (LLMs) are increasingly required to generate text that is both factually accurate and diverse across various open-ended applications. However, current stochas…
Towards a Unified View of Preference Learning for Large Language Models: A Survey
Bofei Gao, Feifan Song, Yibo Miao +22
Large Language Models (LLMs) exhibit remarkably powerful capabilities. One of the crucial factors to achieve success is aligning the LLM's output with human preferences. This align…
Scaling Data Diversity for Fine-Tuning Language Models in Human Alignment
Feifan Song, Bowen Yu, Hao Lang +4
Alignment with human preference prevents large language models (LLMs) from generating misleading or toxic content while requiring high-cost human feedback. Assuming resources of hu…
ICDPO: Effectively Borrowing Alignment Capability of Others via In-context Direct Preference Optimization
Feifan Song, Yuxuan Fan, Xin Zhang +2
Large Language Models (LLMs) rely on Human Preference Alignment (HPA) to ensure the generation of safe content. Due to the heavy cost associated with fine-tuning, fine-tuning-free…
Making Large Language Models Better Reasoners with Alignment
Peiyi Wang, Lei Li, Liang Chen +5
Reasoning is a cognitive process of using evidence to reach a sound conclusion. The reasoning capability is essential for large language models (LLMs) to serve as the brain of the…
Preference Ranking Optimization for Human Alignment
Feifan Song, Bowen Yu, Minghao Li +4
Large language models (LLMs) often contain misleading content, emphasizing the need to align them with human values to ensure secure AI systems. Reinforcement learning from human f…
API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs
Minghao Li, Yingxiu Zhao, Bowen Yu +6
Recent research has demonstrated that Large Language Models (LLMs) can enhance their capabilities by utilizing external tools. However, three pivotal questions remain unanswered: (…
A Unified Framework for Multi-intent Spoken Language Understanding with prompting
Feifan Song, Lianzhe Huang, Houfeng Wang
Multi-intent Spoken Language Understanding has great potential for widespread implementation. Jointly modeling Intent Detection and Slot Filling in it provides a channel to exploit…