Mitigating Hallucinations in Large Language Models through Diversity-Aware Active Learning for Text Summarization
This paper proposes an active learning framework to effectively and efficiently mitigate hallucinations in large language models (LLMs) for text summarization by selecting diverse hallucination samples for annotation and finetuning.
Mitigating Hallucinations in Large Language Models through Targeted Interventions
Effective strategies for mitigating hallucinations in large language models through targeted interventions in specific model components.
Mitigating Hallucinations in Large Language Models via Conformal Abstention
Large language models can confidently generate responses that are incorrect or nonsensical (hallucinations). This work proposes a principled procedure to determine when the model should abstain from responding, instead of hallucinating, by leveraging the model's self-consistency as a measure of confidence.
Enhancing Factual Accuracy of Large Language Models through Adaptive Retrieval Augmentation
Rowen, a novel framework that enhances large language models (LLMs) with an adaptive retrieval augmentation process to effectively mitigate hallucinated outputs.
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