Pre-trained Language Models Return Distinguishable Probability Distributions to Unfaithfully Hallucinated Texts

Autor:	Cha, Taehun, Lee, Donghun
Rok vydání:	2024
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	In this work, we show the pre-trained language models return distinguishable generation probability and uncertainty distribution to unfaithfully hallucinated texts, regardless of their size and structure. By examining 24 models on 6 data sets, we find out that 88-98% of cases return statistically significantly distinguishable generation probability and uncertainty distributions. Using this general phenomenon, we showcase a hallucination-reducing training algorithm. Our algorithm outperforms other baselines by achieving higher faithfulness metrics while maintaining sound general text quality measures. Comment: 10 pages, EMNLP 2024 Findings
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2409.16658 Zobrazit plný text záznamu View this record from Arxiv