Papers with SelfCheckGPT
Detecting Omissions in LLM-Generated Medical Summaries (2025.emnlp-industry)
Copied to clipboard
Achir Oukelmoun, Nasredine Semmar, Gaël de Chalendar, Clement Cormi, Mariame Oukelmoun, Eric Vibert, Marc-Antoine Allard
| Challenge: | Large Language Models (LLMs) have created a number of use cases in the medical field . omissions in summaries can jeopardize the decision-making process . |
| Approach: | They propose a dataset to evaluate omissions in large-scale medical summaries . they propose 'embedKDECheck' method that uses embeddings generated by a third-party NLP model . |
| Outcome: | The proposed method is well-suited for resource-constrained environments. |
SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing fact-checking approaches require access to external databases or external databases . a lack of external databases can undermine trust in large language models. |
| Approach: | They propose a sampling-based approach to fact-check black-box models without external databases. |
| Outcome: | The proposed approach can be used to fact-check black-box models without external databases . it can detect non-factual and factual sentences and rank passages in terms of factuality . |