Closing the Gap Between Text and Speech Understanding in LLMs
Abstract
Large Language Models (LLMs) can be adapted to extend their text capabilities to speech inputs. However, these speech-adapted LLMs consistently underperform their text-based counterparts--and even cascaded pipelines--on language understanding tasks. We term this shortfall the text-speech understanding gap: the performance drop observed when a speech-adapted LLM processes spoken inputs relative to when the original text-based LLM processes the equivalent text. Recent approaches to narrowing this gap either rely on large-scale speech synthesis of text corpora, which is costly and heavily dependent on synthetic data, or on large-scale proprietary speech datasets, which are not reproducible. As a result, there remains a need for more data-efficient alternatives for closing the text-speech understanding gap. In this work, we analyze the gap as driven by two factors: (i) forgetting of text capabilities during adaptation, and (ii) cross-modal misalignment between speech and text. Based on this analysis, we introduce SALAD--Sample-efficient Alignment with Learning through Active selection and cross-modal Distillation--which combines cross-modal distillation with targeted synthetic data to improve alignment while mitigating forgetting. Applied to 3B and 7B LLMs, SALAD achieves competitive performance with a strong open-weight model across broad-domain benchmarks in knowledge, language understanding, and reasoning, while training on over an order of magnitude less speech data from public corpora.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of SALAD, a sample-efficient method for closing the text-speech understanding gap in LLMs, which combines innovative techniques to improve performance while reducing data requirements. This work is significant as it addresses a critical challenge in the field of machine learning, particularly in the context of speech processing and natural language understanding, paving the way for more efficient and effective AI systems.
Methodology
The paper introduces SALAD, a novel approach that combines cross-modal distillation with active selection to address the text-speech understanding gap in LLMs. The methodology is well-structured, consisting of two stages: the first focuses on distillation using natural speech data, while the second employs an active selection strategy to improve domain coverage with synthetic data. The authors provide a thorough analysis of forgetting and cross-modal misalignment, which are critical factors in the performance of speech-adapted LLMs. The use of statistical measures to quantify these factors adds rigor to the methodology.
Results & evaluation
The experiments are comprehensive, evaluating SALAD on multiple broad-domain benchmarks and comparing it against existing models. The results demonstrate that SALAD achieves competitive performance while using significantly less training data, indicating the effectiveness of the proposed method. The benchmarks chosen are relevant and provide a good assessment of the model's capabilities in language understanding and reasoning tasks.
Reproducibility
The paper provides sufficient details regarding the experimental setup, including model architecture, training procedures, and datasets used. However, the lack of URLs for code or data repositories limits reproducibility. The authors mention using publicly available datasets, but specific details on how to access or replicate the experiments are not provided.
Limitations
One limitation is the reliance on specific datasets, which may not cover all aspects of natural speech variability. Additionally, while the active selection method shows promise, its effectiveness may vary depending on the specific domains targeted. The paper also does not address the potential biases introduced by the datasets used, which could impact the generalizability of the findings.
Broader impact
The work has significant implications for the development of more efficient speech-adapted LLMs, potentially enabling broader applications in natural language processing tasks that involve speech inputs. By reducing the reliance on large-scale proprietary datasets, the approach could democratize access to advanced speech understanding technologies, making them more accessible for various applications. The main contribution of this paper is the introduction of SALAD, a sample-efficient method for closing the text-speech understanding gap in LLMs, which combines innovative techniques to improve performance while reducing data requirements. This work is significant as it addresses a critical challenge in the field of machine learning, particularly in the context of speech processing and natural language understanding, paving the way for more efficient and effective AI systems.
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