MetaCheckGPT -- A Multi-task Hallucination Detector Using LLM Uncertainty and Meta-models
This addresses the critical issue of hallucinations in LLMs for users relying on accurate text generation, though it is incremental as it builds on existing competition tasks.
The authors tackled the problem of detecting hallucinations in large language models by developing a meta-regressor framework that achieved first and second place in two sub-tasks of the Semeval 2024 Task 6 competition, outperforming models like GPT-4 in error analysis.
Hallucinations in large language models (LLMs) have recently become a significant problem. A recent effort in this direction is a shared task at Semeval 2024 Task 6, SHROOM, a Shared-task on Hallucinations and Related Observable Overgeneration Mistakes. This paper describes our winning solution ranked 1st and 2nd in the 2 sub-tasks of model agnostic and model aware tracks respectively. We propose a meta-regressor framework of LLMs for model evaluation and integration that achieves the highest scores on the leaderboard. We also experiment with various transformer-based models and black box methods like ChatGPT, Vectara, and others. In addition, we perform an error analysis comparing GPT4 against our best model which shows the limitations of the former.