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arxiv: 2604.09021 · v1 · submitted 2026-04-10 · 💻 cs.SD · cs.AI

Recognition: no theorem link

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

Qixuan Huang , Khalid Zaman , Masashi Unoki
Authors on Pith no claims yet

Pith reviewed 2026-05-10 17:34 UTC · model grok-4.3

classification 💻 cs.SD cs.AI
keywords auditory large language modelshallucination mitigationin-context learningnoise prior libraryaudio captioningClotho-1K datasetALLMs
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The pith

Noise examples added to prompts reduce hallucinations in auditory language models from 26.53 percent to 16.98 percent.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Auditory large language models frequently invent details in their outputs when the input audio lacks clear evidence for certain elements. The paper proposes a plug-and-play method called Noise-Aware In-Context Learning that builds a library of noise examples and retrieves the most relevant ones to add to the model's prompt. These examples act as priors that steer the model toward more cautious descriptions instead of speculative ones. The authors also create a dedicated benchmark for audio captioning that defines four hallucination types, releases the Clotho-1K dataset, and introduces distribution metrics for finer evaluation. The approach targets improved reliability at low cost by avoiding any model retraining.

Core claim

By constructing a noise prior library, retrieving noise examples relevant to the input audio, and incorporating them as contextual priors, the NAICL method guides ALLMs to reduce speculative associations when acoustic evidence is insufficient and to adopt a more conservative generation strategy in audio caption tasks.

What carries the argument

Noise-Aware In-Context Learning (NAICL), which builds and queries a noise prior library to supply relevant noise examples as in-context priors that promote conservative output.

If this is right

  • All evaluated ALLMs exhibit the same hallucination behaviors in audio caption tasks.
  • NAICL lowers the overall hallucination rate from 26.53 percent to 16.98 percent across models.
  • The method provides a plug-and-play alternative that avoids the computational cost of fine-tuning.
  • The Clotho-1K dataset and hallucination type distribution metrics enable fine-grained analysis of error patterns.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The retrieval-based prompting idea could transfer to text-only models for handling ambiguous or low-evidence queries.
  • Careful selection of noise examples might preserve or even improve performance on non-hallucination metrics such as fluency.
  • Expanding the noise library with domain-specific recordings could further reduce errors in specialized audio settings like speech or environmental sound.

Load-bearing premise

Noise examples retrieved from the prior library can be added to the prompt to encourage conservative generation without creating new errors or lowering accuracy on clear audio inputs.

What would settle it

An evaluation on clean audio inputs showing that NAICL increases the hallucination rate or lowers caption quality relative to the baseline model without noise examples.

Figures

Figures reproduced from arXiv: 2604.09021 by Khalid Zaman, Masashi Unoki, Qixuan Huang.

Figure 2
Figure 2. Figure 2 [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Workflow of NAICL, illustrating the retrieval of noise-description pairs for calibrated inference. model noise as an acoustic lower-bound prior, characterizing the generation behavior that should be adopted when reliable semantic cues are insufficient. Accordingly, we propose the NAICL method, as shown in [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
read the original 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%.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper introduces Noise-Aware In-Context Learning (NAICL) as a plug-and-play method to mitigate hallucinations in Auditory Large Language Models (ALLMs) by constructing a noise prior library, retrieving relevant noise examples, and incorporating them as contextual priors to promote conservative generation when acoustic evidence is weak. It also presents a new hallucination benchmark for audio caption tasks, featuring the Clotho-1K multi-event dataset, definitions of four auditory hallucination types, and metrics like hallucination type distribution. The key experimental result is a reduction in overall hallucination rate from 26.53% to 16.98% for evaluated ALLMs.

Significance. If validated, this work offers a computationally efficient alternative to fine-tuning for hallucination mitigation in ALLMs and advances evaluation practices by moving beyond binary hallucination classification to fine-grained type analysis. The creation of the Clotho-1K benchmark and consistent hallucination patterns observed across models provide valuable resources for the community.

major comments (2)
  1. [Experimental Results] The central claim that the reduction from 26.53% to 16.98% is attributable to the noise-aware mechanism requires an ablation study. Replacing retrieved noise examples with random or unrelated ones from the same library would isolate whether gains arise from targeted noise priors rather than generic in-context learning or added prompt length; the current no-context baseline alone does not establish this.
  2. [Abstract and Experimental Results] The reported hallucination rates and benchmark results lack essential details on experimental setup: how the four hallucination types were annotated for Clotho-1K, choice of baselines, number of models evaluated, and any statistical significance testing. These omissions leave the numerical claim weakly supported.
minor comments (1)
  1. [Abstract] The abstract phrase 'exhibit same hallucination behaviors' should read 'exhibit the same hallucination behaviors' for grammatical accuracy.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their insightful comments, which have helped us improve the clarity and rigor of our work. Below, we provide point-by-point responses to the major comments. We have revised the manuscript to address the concerns raised.

read point-by-point responses

  1. Referee: [Experimental Results] The central claim that the reduction from 26.53% to 16.98% is attributable to the noise-aware mechanism requires an ablation study. Replacing retrieved noise examples with random or unrelated ones from the same library would isolate whether gains arise from targeted noise priors rather than generic in-context learning or added prompt length; the current no-context baseline alone does not establish this.

    Authors: We agree with the referee that an ablation study is necessary to isolate the contribution of the targeted noise retrieval. We will add an experiment replacing the retrieved noise examples with random selections from the same library in the revised version of the manuscript. This will allow us to demonstrate whether the performance gains are due to the relevance of the noise priors or simply from additional in-context examples. revision: yes

  2. Referee: [Abstract and Experimental Results] The reported hallucination rates and benchmark results lack essential details on experimental setup: how the four hallucination types were annotated for Clotho-1K, choice of baselines, number of models evaluated, and any statistical significance testing. These omissions leave the numerical claim weakly supported.

    Authors: We appreciate this feedback and have revised the abstract and the experimental results section to include the missing details. Specifically, we now describe the annotation process for the four hallucination types in Clotho-1K, including the guidelines provided to annotators. We specify the ALLMs used in our evaluations, justify the selection of baselines, and report the results of statistical significance tests confirming the reliability of the hallucination rate reductions. These clarifications ensure the experimental claims are better supported. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical method with independent experimental support

full rationale

The paper describes a procedural NAICL method (construct noise prior library, retrieve relevant examples, incorporate as context) and reports direct experimental outcomes on hallucination rates and a new benchmark. No equations, derivations, fitted parameters, or uniqueness theorems appear in the text. Claims do not reduce to self-definitions or self-citations by construction; results are presented as measured differences versus baselines rather than tautological predictions. This is the common case of a self-contained experimental paper.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The approach rests on standard assumptions about in-context learning effectiveness and the representativeness of a constructed noise library, with no free parameters, axioms, or invented entities explicitly introduced beyond conventional ML components.

pith-pipeline@v0.9.0 · 5523 in / 1198 out tokens · 46983 ms · 2026-05-10T17:34:56.560062+00:00 · methodology

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Reference graph

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    Introduction Auditory large language models (ALLMs) have demonstrated strong general capabilities in audio understanding and reason- ing [1, 2]. However, in real world audio scenarios characterized by overlapping events, background noise, and pervasive acous- tic–semantic uncertainty, models tend to rely on linguistic priors to produce overly deterministi...

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    Benchmark Designing 2.1. Dataset Preprocessing and Filtering Clotho is a crowdsourced dataset for audio captioning in which each audio clip is independently annotated with natural lan- guage descriptions by five annotators. It comprises approxi- mately 4,981 audio samples. Based on the Clotho dataset, we design and conduct a multi-stage manual filtering a...

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