@inproceedings{
sriramanan2024llmcheck,
title={{LLM}-Check: Investigating Detection of Hallucinations in Large Language Models},
author={Gaurang Sriramanan and Siddhant Bharti and Vinu Sankar Sadasivan and Shoumik Saha and Priyatham Kattakinda and Soheil Feizi},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=LYx4w3CAgy}
}
While Large Language Models (LLMs) have become immensely popular due to their outstanding performance on a broad range of tasks, these models are prone to producing hallucinations— outputs that are fallacious or fabricated yet often appear plausible or tenable at a glance. In this paper, we conduct a comprehensive investigation into the nature of hallucinations within LLMs and furthermore explore effective techniques for detecting such inaccuracies in various real-world settings. Prior approaches to detect hallucinations in LLM outputs, such as consistency checks or retrieval-based methods, typically assume access to multiple model responses or large databases. These techniques, however, tend to be computationally expensive in practice, thereby limiting their applicability to real-time analysis. In contrast, in this work, we seek to identify hallucinations within a single response in both white-box and black-box settings by analyzing the internal hidden states, attention maps, and output prediction probabilities of an auxiliary LLM. In addition, we also study hallucination detection in scenarios where ground-truth references are also available, such as in the setting of Retrieval-Augmented Generation (RAG). We demonstrate that the proposed detection methods are extremely compute-efficient, with speedups of up to 45x and 450x over other baselines, while achieving significant improvements in detection performance over diverse datasets.
LLM-Check: Investigating Detection of Hallucinations in Large Language Models
Gaurang Sriramanan Siddhant Bharti Vinu Sankar Sadasivan Shoumik Saha Priyatham Kattakinda Soheil Feizi