Noise-Aware In-Context Learning for Hallucination Mitigation in ALLMs

TL;DR

This paper introduces a noise-aware in-context learning method to mitigate hallucinations in auditory large language models, improving reliability without fine-tuning.

Contribution

It proposes a plug-and-play approach using a noise prior library and establishes a new hallucination benchmark for audio captioning tasks.

Findings

NAICL reduces hallucination rate from 26.53% to 16.98%.

All evaluated ALLMs exhibit similar hallucination behaviors.

Constructed Clotho-1K multi-event benchmark dataset.

Abstract

Auditory large language models (ALLMs) have demonstrated strong general capabilities in audio understanding and reasoning tasks. However, their reliability is still undermined by hallucination issues. Existing hallucination evaluation methods are formulated as binary classification tasks, which are insufficient to characterize the more complex hallucination patterns that arise in generative tasks. Moreover, current hallucination mitigation strategies rely on fine-tuning, resulting in high computational costs. To address the above limitations, we propose a plug-and-play Noise-Aware In-Context Learning (NAICL) method. Specifically, we construct a noise prior library, retrieve noise examples relevant to the input audio, and incorporate them as contextual priors, thereby guiding the model to reduce speculative associations when acoustic evidence is insufficient and to adopt a more conservative generation strategy. In addition, we establish a hallucination benchmark for audio caption tasks including the construction of the Clotho-1K multi-event benchmark dataset, the definition of four types of auditory hallucinations, and the introduction of metrics such as hallucination type distribution to support fine-grained analysis. Experimental results show that all evaluated ALLMs exhibit same hallucination behaviors. Moreover, the proposed NAICL method reduces the overall hallucination rate from 26.53% to 16.98%.