3 papers
cs.CL2025
OpenGenAlign: A Preference Dataset and Benchmark for Trustworthy Reward Modeling in Open-Ended, Long-Context Generation
Hanning Zhang, Juntong Song, Juno Zhu +3
Reward Modeling is critical in evaluating and improving the generation of Large Language Models (LLMs). While numerous recent works have shown its feasibility in improving safety,…
cs.CL2024
VeraCT Scan: Retrieval-Augmented Fake News Detection with Justifiable Reasoning
Cheng Niu, Yang Guan, Yuanhao Wu +7
The proliferation of fake news poses a significant threat not only by disseminating misleading information but also by undermining the very foundations of democracy. The recent adv…
cs.CL2024
RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models
Cheng Niu, Yuanhao Wu, Juno Zhu +5
Retrieval-augmented generation (RAG) has become a main technique for alleviating hallucinations in large language models (LLMs). Despite the integration of RAG, LLMs may still pres…