AudioBERT: Audio Knowledge Augmented Language Model (2409.08199v1)

Abstract: Recent studies have identified that LLMs, pretrained on text-only datasets, often lack elementary visual knowledge, \textit{e.g.,} colors of everyday objects. Motivated by this observation, we ask whether a similar shortcoming exists in terms of the \textit{auditory} knowledge. To answer this question, we construct a new dataset called AuditoryBench, which consists of two novel tasks for evaluating auditory knowledge. Based on our analysis using the benchmark, we find that LLMs also suffer from a severe lack of auditory knowledge. To address this limitation, we propose AudioBERT, a novel method to augment the auditory knowledge of BERT through a retrieval-based approach. First, we detect auditory knowledge spans in prompts to query our retrieval model efficiently. Then, we inject audio knowledge into BERT and switch on low-rank adaptation for effective adaptation when audio knowledge is required. Our experiments demonstrate that AudioBERT is quite effective, achieving superior performance on the AuditoryBench. The dataset and code are available at \bulurl{https://github.com/HJ-Ok/AudioBERT}.

• The paper introduces AudioBERT, which augments BERT with audio embeddings using CLAP and LoRA to enhance multimodal capabilities and improve accuracy by over 40%.
• It leverages a novel dataset, AuditoryBench, to evaluate performance on tasks like animal sound recognition and pitch comparison, addressing gaps in text-only models.
• The study highlights the potential of integrating auditory cues into language models, paving the way for advanced multimodal systems in assistive technologies and scene analysis.

A Comprehensive Analysis of "AudioBERT: Audio Knowledge Augmented LLM"

The paper "AudioBERT: Audio Knowledge Augmented LLM" addresses a significant gap in natural language processing by integrating auditory knowledge into LLMs. Traditional pre-trained LLMs, such as BERT and its derivatives, are well-regarded for their proficiency in various language understanding tasks. However, they are often limited by their reliance on text-only datasets, lacking in both visual and auditory commonsense knowledge. This limitation is particularly evident in domains where multimodal understanding—specifically, auditory information—is crucial.

Contributions and Methodology

The authors contribute a novel dataset, AuditoryBench, designed to evaluate LLMs on tasks requiring auditory knowledge. AuditoryBench comprises two main tasks: animal sound recognition and sound pitch comparison. These tasks were constructed using a pipeline based on LLMs to ensure scalability and quality, producing datasets that account for various sound categories with a hierarchical approach.

To augment LLMs with auditory capabilities, the authors propose AudioBERT, a retrieval-based framework that utilizes audio embeddings. Central to this framework is the use of CLAP (Contrastive Language-Audio Pretraining), which facilitates audio retrieval based on textual cues by exploiting the audio-text similarity space. Enhancements in the architecture include an auditory knowledge span detector, which identifies relevant text spans where auditory knowledge is necessary. By dynamically injecting audio embeddings using LoRA (Low-Rank Adaptation) into a BERT-based model, the approach ensures minimal disruption to the model's general language understanding capabilities.

Numerical Results

The empirical evaluation, focusing on benchmarks such as BERT and LLaMA, reveals a notable deficiency in auditory knowledge within traditional LLMs. AudioBERT significantly improves prediction accuracy on the AuditoryBench by more than 40% over the test set. Specifically, AudioBERT demonstrates competitive accuracy in both animal sound recognition and sound pitch comparison tasks, considerably outperforming baseline LLMs like BERT and RoBERTa.

Implications and Future Directions

Practically, AudioBERT presents a promising stride towards enhancing LLMs for use cases that require auditory context, such as assistive technologies and auditory scene analysis. Theoretically, this work bridges the gap in multimodal research by highlighting the effective incorporation of non-verbal cues into LLMs.

Future directions may explore further integration of auditory and visual data into LLMs to develop more comprehensive multimodal systems. Additionally, refining the auditory span detection and expanding the auditory corpus used for training may increase model performance and applicability across varied auditory tasks.

This paper sets the groundwork for more robust multimodal AI systems, which can seamlessly process and interpret a combination of text, auditory, and visual inputs, thereby advancing the capability of AI in understanding and interacting with the real world.