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Audio Entailment: Assessing Deductive Reasoning for Audio Understanding
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Introduces Audio Entailment, a task testing deductive reasoning of audio-language models via entailment/neutral/contradiction labels on two LLM-generated, human-verified datasets (ACE, CLE), and proposes caption-before-reason to improve zero-shot and linear-probe performance.
Problem
Audio-language models are benchmarked on classification, retrieval, and open-ended QA, but their deductive reasoning over audio premises and textual hypotheses has not been evaluated.
Approach
Frame audio-text deduction as a 3-way entailment task built from AudioCaps/Clotho recordings and LLM-generated hypotheses verified by humans, and add an intermediate audio-captioning step ("caption-before-reason") before reasoning in zero-shot prompting and linear probes.
Key results
- Caption-before-reason raises Qwen-AC zero-shot F1 on CLE from 0.4975 to 0.5601 (about 6 points).
- Linear probe on CLAP 2023 with caption step reaches F1 0.8647 versus 0.8336 with concatenated embeddings (about 3 points).
- Best zero-shot F1 is around 51% on CLE and 56% on ACE, indicating limited deductive reasoning in current ALMs.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Caption audio before reasoning (ablation) | Qwen-AC · CLE zero-shot | F1: 0.4975 -> 0.5601 | Helped |
| Add caption embeddings before classification (ablation) | MS CLAP 23 linear probe · CLE | F1: 0.8336 -> 0.8647 | Helped |
| Add the audio encoder (ablation) | Pengi · CLE zero-shot | F1: 0.2216 -> 0.2888 | Helped |
| Add the audio encoder (ablation) | Pengi · ACE zero-shot | F1: 0.2045 -> 0.3039 | Helped |
| Add instruction tuning stage (ablation) | GAMA · CLE zero-shot | F1: 0.4534 -> 0.3433 | Hurt |
| Add instruction tuning stage (ablation) | GAMA · ACE zero-shot | F1: 0.4933 -> 0.3828 | Hurt |
| Use average instead of concatenation for representations (ablation) | MS CLAP 23 linear probe · CLE | F1: 0.8336 -> 0.7515 | Hurt |
| Sum instead of concatenate representations (ablation) | MS CLAP 23 linear probe · CLE | F1: 0.8336 -> 0.7785 | Hurt |
| Use the detailed question prompt instead of the short prompt “Is it true?” (ablation) | LTU-AS · CLE zero-shot | F1: 0.3420 -> 0.1734 | Hurt |
| Use the short prompt “Is it true?” instead of the detailed question prompt (ablation) | LTU-AS · CLE zero-shot | F1: 0.1734 -> 0.3420 | Helped |
| Use caption-before-reason prompting (ablation) | Qwen-AC · CLE zero-shot | contradiction accuracy: 0.5732 -> 0.8057 | Helped |
| Use caption-before-reason prompting (ablation) | Qwen-AC · CLE zero-shot | entailment accuracy: 0.9024 -> 0.8392 | Hurt |
Surprising findings
- Contrastive models perform competitively with next-token prediction ALMs on the entailment task despite not generating text.
- Changing minor prompt wording (e.g., stopwords) substantially changes LTU-AS and GAMA predictions, with some ALMs collapsing to always answering entailment.
Limitations
- Hypotheses are generated by LLMs and only partially verified by humans, which may introduce bias or world-knowledge leakage.
- Next-token ALM outputs are classified by LLM judges, with 94-99% agreement to human labels rather than perfect parsing.
- Zero-shot contrastive results depend on thresholds tuned on the validation set.
Experimental setup
Datasets: AudioCaps Clotho ClothoAQA OpenAQA
Benchmarks: AudioCaps Entailment (ACE) Clotho Entailment (CLE)
Metrics: accuracy precision recall F1 per-class accuracy (EACC, NACC, CACC)
Baselines: MS CLAP 2022 LAION CLAP MS CLAP 2023 Pengi LTU-AS Qwen-Audio Qwen-Audio-Chat GAMA GAMA-IT SALMONN
Builds on: CLAP Pengi LTU-AS Qwen-Audio GAMA
Closest papers by meaning
- Mellow: a small audio language model for reasoning (NeurIPS 2025 · similarity 0.92)
- MMAR: A Challenging Benchmark for Deep Reasoning in Speech, Audio, Music, and Their Mix (NeurIPS 2025 · similarity 0.91)
- AudSemThinker: Enhancing Audio-Language Models Through Reasoning over Semantics of Sound (NeurIPS 2025 · similarity 0.89)
- JointAVBench: A Benchmark for Joint Audio-Visual Reasoning Evaluation (ICLR 2026 · similarity 0.88)
- Audio Flamingo 2: An Audio-Language Model with Long-Audio Understanding and Expert Reasoning Abilities (ICML 2025 · similarity 0.88)
- MECAT: A Multi-Experts Constructed Benchmark for Fine-Grained Audio Understanding Tasks (ICML 2026 · similarity 0.88)
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