55 citations · 84 across the 8 of their papers we have counts for
8 papers · 1 filter
ECon: On the Detection and Resolution of Evidence Conflicts
Cheng Jiayang, Chunkit Chan, Qianqian Zhuang +7
The rise of large language models (LLMs) has significantly influenced the quality of information in decision-making systems, leading to the prevalence of AI-generated content and c…
RAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generation
Dongyu Ru, Lin Qiu, Xiangkun Hu +15
Despite Retrieval-Augmented Generation (RAG) showing promising capability in leveraging external knowledge, a comprehensive evaluation of RAG systems is still challenging due to th…
RefChecker: Reference-based Fine-grained Hallucination Checker and Benchmark for Large Language Models
Xiangkun Hu, Dongyu Ru, Lin Qiu +7
Large Language Models (LLMs) have shown impressive capabilities but also a concerning tendency to hallucinate. This paper presents RefChecker, a framework that introduces claim-tri…
RepEval: Effective Text Evaluation with LLM Representation
Shuqian Sheng, Yi Xu, Tianhang Zhang +7
The era of Large Language Models (LLMs) raises new demands for automatic evaluation metrics, which should be adaptable to various application scenarios while maintaining low cost a…
SH2: Self-Highlighted Hesitation Helps You Decode More Truthfully
Jushi Kai, Tianhang Zhang, Hai Hu +1
Large language models (LLMs) demonstrate great performance in text generation. However, LLMs are still suffering from hallucinations. In this work, we propose an inference-time met…
GeoGalactica: A Scientific Large Language Model in Geoscience
Zhouhan Lin, Cheng Deng, Le Zhou +18
Large language models (LLMs) have achieved huge success for their general knowledge and ability to solve a wide spectrum of tasks in natural language processing (NLP). Due to their…
Enhancing Uncertainty-Based Hallucination Detection with Stronger Focus
Tianhang Zhang, Lin Qiu, Qipeng Guo +6
Large Language Models (LLMs) have gained significant popularity for their impressive performance across diverse fields. However, LLMs are prone to hallucinate untruthful or nonsens…
Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity
Cunxiang Wang, Xiaoze Liu, Yuanhao Yue +13
This survey addresses the crucial issue of factuality in Large Language Models (LLMs). As LLMs find applications across diverse domains, the reliability and accuracy of their outpu…