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

TL;DR

This paper demonstrates that pre-trained language models produce distinguishable probability and uncertainty distributions for hallucinated texts, enabling a new training method that reduces hallucinations while preserving text quality.

Contribution

The study reveals a general phenomenon across models and datasets, and introduces a novel hallucination-reducing training algorithm based on distribution distinguishability.

Findings

88-98% of cases show distinguishable distributions

Proposed algorithm outperforms baselines in faithfulness metrics

Maintains high general text quality

Abstract

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.