Differences

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--- nlp:hallucination_and_factivity [2025/04/01 21:32] – [Datasets] jmflanig
+++ nlp:hallucination_and_factivity [2025/06/02 00:52] (current) – [Hallucination and Factivity in LLMs] jmflanig
@@ Line 14: / Line 14: @@
   * [[https://arxiv.org/pdf/2206.04624.pdf|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.
   * [[https://arxiv.org/pdf/2210.09150.pdf|Prompting GPT-3 To Be Reliable]]
+  * [[https://arxiv.org/pdf/2305.13534|Zhang et al 2023 - How Language Model Hallucinations Can Snowball]]
   * **[[https://arxiv.org/pdf/2305.14251.pdf|Min et al 2023 - FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation]]**
   * [[https://arxiv.org/pdf/2309.05217.pdf|Du et al 2023 - Quantifying and Attributing the Hallucination of Large Language Models via Association Analysis]]
@@ Line 19: / Line 20: @@
   * [[https://arxiv.org/pdf/2310.00259.pdf|Zouying et al 2023 - AutoHall: Automated Hallucination Dataset Generation for Large Language Models]]
   * [[https://arxiv.org/pdf/2310.00741.pdf|Chen et al 2023 - FELM: Benchmarking Factuality Evaluation of Large Language Models]] [[https://github.com/hkust-nlp/felm|github]]
-  * **[[https://arxiv.org/pdf/2311.08401.pdf|Tian et al 2023 - Fine-tuning Language Models for Factuality]]**
+  * **[[https://arxiv.org/pdf/2311.08401.pdf|Tian et al 2023 - Fine-tuning Language Models for Factuality]]** Uses DPO to fine-tune an LLM to produce factual outputs
   * [[https://arxiv.org/pdf/2404.05904|Hong et al 2024 - The Hallucinations Leaderboard – An Open Effort to Measure Hallucinations in Large Language Models]]
   * **[[https://arxiv.org/pdf/2405.05904|Gekhman et al 2024 - Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?]]**