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Audio-language models often answer questions without audio, challenging evaluation methods.

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 4 sources

New research indicates that Large Audio-Language Models (LALMs) may not possess true auditory perception despite high benchmark scores. Studies reveal that these models can answer questions using only text and general knowledge, retaining a significant portion of their performance without audio input. Furthermore, when audio is necessary, models often only require localized fragments rather than complete clips, challenging the reliability of current evaluation methods for assessing robust audio understanding. AI

Summary written by gemini-2.5-flash-lite from 4 sources. How we write summaries →

IMPACT Challenges current evaluation metrics for audio-language models, suggesting a need for more robust benchmark designs that accurately measure auditory understanding.

RANK_REASON The cluster contains two academic papers published on arXiv concerning the evaluation of Large Audio-Language Models.

Read on arXiv cs.CL →

COVERAGE [4]

  1. arXiv cs.CL TIER_1 · Leonardo Haw-Yang Foo, Chih-Kai Yang, Chen-An Li, Ke-Han Lu, Hung-yi Lee ·

    All That Glitters Is Not Audio: Rethinking Text Priors and Audio Reliance in Audio-Language Evaluation

    arXiv:2604.24401v1 Announce Type: cross Abstract: Large Audio-Language Models show consistent performance gains across speech and audio benchmarks, yet high scores may not reflect true auditory perception. If a model can answer questions without processing the acoustic signal, th…

  2. arXiv cs.CL TIER_1 · Chen-An Li, Tzu-Han Lin, Hung-yi Lee ·

    When Silence Matters: The Impact of Irrelevant Audio on Text Reasoning in Large Audio-Language Models

    arXiv:2510.00626v3 Announce Type: replace-cross Abstract: Large audio-language models (LALMs) unify speech and text processing, but their robustness in noisy real-world settings remains underexplored. We investigate how irrelevant audio, such as silence, synthetic noise, and envi…

  3. arXiv cs.CL TIER_1 · Hung-yi Lee ·

    All That Glitters Is Not Audio: Rethinking Text Priors and Audio Reliance in Audio-Language Evaluation

    Large Audio-Language Models show consistent performance gains across speech and audio benchmarks, yet high scores may not reflect true auditory perception. If a model can answer questions without processing the acoustic signal, the benchmark fails as a measure of auditory underst…

  4. Hugging Face Daily Papers TIER_1 ·

    All That Glitters Is Not Audio: Rethinking Text Priors and Audio Reliance in Audio-Language Evaluation

    Large Audio-Language Models show consistent performance gains across speech and audio benchmarks, yet high scores may not reflect true auditory perception. If a model can answer questions without processing the acoustic signal, the benchmark fails as a measure of auditory underst…

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