Exploring and Mitigating Fawning Hallucinations in Large Language Models

Large language models (LLMs) have demonstrated exceptional proficiency in language understanding. However, when LLMs align their outputs with deceptive and / or misleading prompts, the generated responses could deviate from the de facto information. Such observations are known as fawning hallucinations, where the model prioritizes alignment with the input's implied perspective over accuracy and truthfulness. In this work, we analyze fawning hallucinations in various natural language processing tasks and tailor the so-termed contrastive decoding method for fawning-hallucination mitigation. Specifically, we design two paradigms to generate corresponding deceptive and / or misleading inputs for the consistent fawning hallucinations induction. Then, we propose the collaborative contrastive decoding (CCD) to handle the fawning hallucinations across di ff erent tasks in LLMs. By contrasting the deviation in output distribution between induced and transformed neutral inputs, the proposed CCD can reduce reliance on deceptive and / or misleading information without requiring additional training. Extensive experiments demonstrate that the proposed CCD can e ff ectively mitigate fawning hallucinations and improve the factuality of the generated responses over various tasks. Introduction Large language models (LLMs), exemplified by the Chat-GPT series [1], have demonstrated their remarkable capabilities across a wide range of natural language processing (NLP) tasks. These tasks include text translation [2, 3], summarization [4, 5], and a ffective computing [6, 7, 8, 9], showcasing the versatility and impact of artificial intelligence (AI). Despite the impressive performance, LLMs are criticized for the potential to generate fabricated, inaccurate, or incorrect information. This phenomenon, known as "hallucination", hinders the further practical application of LLMs.