Papers by Yuanhao Wu
OpenGenAlign: A Preference Dataset and Benchmark for Trustworthy Reward Modeling in Open-Ended, Long-Context Generation (2026.findings-acl)
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| Challenge: | Existing reward models perform suboptimal on held-out benchmarks, resulting in poor quality outputs. |
| Approach: | They propose a framework and a high-quality dataset to evaluate reward models . they define four key metrics to assess generation quality and develop a pipeline to evaluate outputs . |
| Outcome: | The proposed framework and dataset improves hallucination-free, comprehensive, reliable, and efficient open-ended long-context generation. |
RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models (2024.acl-long)
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| Challenge: | Retrieval-augmented generation (RAG) is a main technique for alleviating hallucinations in large language models. |
| Approach: | They propose to integrate RAG into large language models to analyze word-level hallucinations using a corpus of 18,000 naturally generated responses from diverse LLMs. |
| Outcome: | The proposed model can fine tune a relatively small LLM and achieve a competitive hallucination detection performance when compared to the existing prompt-based approaches. |
RAG-HAT: A Hallucination-Aware Tuning Pipeline for LLM in Retrieval-Augmented Generation (2024.emnlp-industry)
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| Challenge: | Retrieval-augmented generation (RAG) has emerged as a significant advancement in the field of large language models (LLMs). |
| Approach: | They propose a method that uses hallucination detection labels to correct hallucines by integrating up-to-date information into their initial training. |
| Outcome: | The proposed method is based on the Retrieval Augmented Generation (RAG) method, which has shown to be effective in mitigating hallucinations and improving answer quality. |
VeraCT Scan: Retrieval-Augmented Fake News Detection with Justifiable Reasoning (2024.acl-demos)
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Cheng Niu, Yang Guan, Yuanhao Wu, Juno Zhu, Juntong Song, Randy Zhong, Kaihua Zhu, Siliang Xu, Shizhe Diao, Tong Zhang
| Challenge: | generative artificial intelligence has exacerbated the challenge of distinguishing genuine news from fabricated stories. |
| Approach: | They propose a retrieval-augmented system that extracts the core facts from a given piece of news and conducts an internet-wide search to identify corroborating or conflicting reports. |
| Outcome: | The proposed system has demonstrated state-of-the-art accuracy in the realm of fake news detection. |