The Two Sides of the Coin: Hallucination Generation and Detection with LLMs as Evaluators for LLMs

Autor:	Bui, Anh Thu Maria, Brech, Saskia Felizitas, Hußfeldt, Natalie, Jennert, Tobias, Ullrich, Melanie, Breuer, Timo, Khasmakhi, Narjes Nikzad, Schaer, Philipp
Rok vydání:	2024
Předmět:	Computer Science - Artificial Intelligence Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	Hallucination detection in Large Language Models (LLMs) is crucial for ensuring their reliability. This work presents our participation in the CLEF ELOQUENT HalluciGen shared task, where the goal is to develop evaluators for both generating and detecting hallucinated content. We explored the capabilities of four LLMs: Llama 3, Gemma, GPT-3.5 Turbo, and GPT-4, for this purpose. We also employed ensemble majority voting to incorporate all four models for the detection task. The results provide valuable insights into the strengths and weaknesses of these LLMs in handling hallucination generation and detection tasks. Comment: Paper accepted at ELOQUENT@CLEF'24
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.09152 Zobrazit plný text záznamu View this record from Arxiv