Audio Entailment: Assessing Deductive Reasoning for Audio Understanding

Audio Entailment: Assessing Deductive Reasoning for Audio Understanding

Abstract

This paper introduces the task of Audio Entailment, designed to evaluate the deductive reasoning abilities of Audio-Language Models (ALMs). With advancements in leveraging language models to build foundation models for audio, tasks such as Text-to-Audio Retrieval, Captioning, and Question Answering have seen significant performance improvements. However, the capability of ALMs to handle more complex, open-ended tasks requiring logical reasoning has not yet been benchmarked. This paper pioneers the evaluation of logical reasoning through the Audio Entailment task, assessing if a textual hypothesis of audio content can be deduced from an audio recording, with potential conclusions being entailment, neutral, or contradiction. The study introduces two datasets, ACE and CLE, derived from AudioCaps and Clotho datasets, and benchmarks state-of-the-art ALMs, revealing deficiencies in logical reasoning capabilities. By proposing an intermediate step of captioning, termed "caption-before-reason," the paper demonstrates performance improvements of 6% and 3% in zero-shot and linear-probe capabilities, respectively.

Introduction

The rise of Audio-Language Models (ALMs), trained on millions of audio-text pairs through contrastive learning or next-token prediction, has enabled a range of audio-grounded tasks. These models are effective in tasks like Text-to-Audio Retrieval and Captioning but have not been evaluated for logical reasoning. To address this gap, the paper introduces Audio Entailment, a task to assess an ALM's deductive reasoning ability. Audio Entailment examines if a text description (hypothesis) can be deduced from an audio recording (premise) with conclusions categorized as entailment, neutral, or contradiction.

Methodology

This study formulates Audio Entailment as a classification task, where the input comprises an audio premise and a textual hypothesis, and the target is a classification among entailment, neutral, or contradiction. Two datasets, ACE and CLE, were created. Audio premises were sourced from the AudioCaps and Clotho datasets, and hypotheses were generated using LLMs, then verified and corrected by human annotators to ensure quality.

Benchmarking ALMs

The paper benchmarks state-of-the-art ALMs using both zero-shot and linear-probe evaluations. Contrastive models such as MS CLAP and LAION CLAP, as well as next-token prediction models like Pengi and LTU-AS, were evaluated. Zero-shot performance revealed that larger language models improved deductive reasoning but were challenging to ground in audio content. An observed phenomenon was that contrastive models, trained using similarity learning, competed closely with next-token prediction models in logical reasoning tasks.

Linear Probe and Representation

The study conducted linear-probe experiments to evaluate the learned audio-text representations. Results indicated that while pretraining with audio-text pairs imbued models with primitive reasoning capabilities, there was substantial room for improvement. The linear-probe approach bypassed thresholding and prompting issues, showcasing robust classification performance over several metrics.

Caption-Before-Reason: An Innovative Approach

A novel approach, "caption-before-reason," was proposed, where an intermediate step of audio captioning is introduced before reasoning with the hypothesis. This method underwent both zero-shot prompting and linear-probe testing. The zero-shot setup showed an absolute improvement of 6% in deductive reasoning capabilities, while the linear-probe setup exhibited a 3% improvement. This approach was found to enhance the model's accuracy in predicting contradictions and reduced hallucinations, thus improving grounding in the audio input.

Conclusion

The introduction of the Audio Entailment task and the corresponding datasets, ACE and CLE, represent significant steps in evaluating and understanding ALMs' deductive reasoning capabilities. The benchmarking exercise reveals the current limitations and identifies areas for improvement, particularly in the context of logical reasoning grounded in audio data. The proposed "caption-before-reason" method demonstrates a practical way to enhance model performance in complex reasoning tasks. Future research directions include refining the pretraining methods to better develop representations conducive to logical reasoning and further reducing hallucinations in model outputs.

Implications and Future Work

The implications of this research span both practical and theoretical domains. Practically, improving ALMs for logical reasoning opens up more reliable interactive applications, such as AI assistants capable of nuanced and context-aware conversations. Theoretically, this study lays the groundwork for future explorations into multimodal reasoning and the integration of more sophisticated logical operations in ALMs. Future developments could focus on more intricate instruction-based tuning and advanced pretraining techniques to build models with enhanced reasoning capabilities and reduced hallucinations, furthering the pursuit of AI that is not only generative but also deeply insightful.