• In Generation
  • Ji et al 2022 - Survey of Hallucination in Natural Language Generation
• In Large Language Models
  • Zhang et al 2023 - Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models
  • Rawte et al 2023 - A Survey of Hallucination in Large Foundation Models
  • Ye et al 2023 - Cognitive Mirage: A Review of Hallucinations in Large Language Models
  • Andriopoulos & Pouwelse 2023 - Augmenting LLMs with Knowledge: A Survey on Hallucination Prevention

• Lin et al 2022 - TruthfulQA: Measuring How Models Mimic Human Falsehoods
• Lee et al 2022 - Factuality Enhanced Language Models for Open-Ended Text Generation - Prepends a topic prefix to sentences in the factual documents to make each sentence serve as a standalone fact during pretraining.
• Prompting GPT-3 To Be Reliable
• Merrill et al 2023 - How Language Model Hallucinations Can Snowball
• Min et al 2023 - FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation
• Du et al 2023 - Quantifying and Attributing the Hallucination of Large Language Models via Association Analysis
• Dhuliawala et al 2023 - Chain-of-Verification Reduces Hallucination in Large Language Models
• Zouying et al 2023 - AutoHall: Automated Hallucination Dataset Generation for Large Language Models
• Chen et al 2023 - FELM: Benchmarking Factuality Evaluation of Large Language Models github
• Tian et al 2023 - Fine-tuning Language Models for Factuality Uses DPO to fine-tune an LLM to produce factual outputs
• Hong et al 2024 - The Hallucinations Leaderboard – An Open Effort to Measure Hallucinations in Large Language Models
• Gekhman et al 2024 - Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?

Prompt to break down sentences into independent facts: (from Min 2023)

• TruthQA: paper github
• FactScore: paper github
• FELM: paper github
• LongFact: paper

• Automatic Fact Checking
• Factivity
• Language Model
• Trustworthy AI