mlsanityData to 7 Oct 2026

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

arXiv 2409.16658 ↗First posted 25 Sep 2024cs.CLcited by 1

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 23 of its 34 references that are in the map. First posted on arXiv 25 Sep 2024.

Builds on

23 of 34 references.

References · 23

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  4. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  5. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  6. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  7. 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,772
  8. 2020BLEURT: Learning Robust Metrics for Text Generationcited by 1,196
  9. 2021BARTScore: Evaluating Generated Text as Text Generationcited by 719
  10. 2023The Internal State of an LLM Knows When It's Lyingcited by 644
  11. 2018Wizard of Wikipedia: Knowledge-Powered Conversational agentscited by 530
  12. 2021SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarizationcited by 402
11 more
  1. 2023A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generationcited by 202
  2. 2021On Hallucination and Predictive Uncertainty in Conditional Language Generationcited by 176
  3. 2022FaithDial: A Faithful Benchmark for Information-Seeking Dialoguecited by 108
  4. 2023Topical-Chat: Towards Knowledge-Grounded Open-Domain Conversationscited by 88
  5. 2020Improved Natural Language Generation via Loss Truncationcited by 75
  6. 2021Evaluating Attribution in Dialogue Systems: The BEGIN Benchmarkcited by 75
  7. 2021Increasing Faithfulness in Knowledge-Grounded Dialogue with Controllable Featurescited by 74
  8. 2023FactKB: Generalizable Factuality Evaluation using Language Models Enhanced with Factual Knowledgecited by 55
  9. 2023Detecting and Mitigating Hallucinations in Multilingual Summarisationcited by 48
  10. 2020Question-Driven Summarization of Answers to Consumer Health Questionscited by 39
  11. 2023Sequence-Level Certainty Reduces Hallucination In Knowledge-Grounded Dialogue Generationcited by 5

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Detecting Spelling and Grammatical Anomalies in Russian Poetry Textscited by 0