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DirectSpeech2LLM framework tackles prompt overfitting in Speech-LLMs

By PulseAugur Editorial · [1 sources] ·

Researchers have developed DirectSpeech2LLM, a novel framework designed to address prompt overfitting in Speech-LLMs. This end-to-end system enables LLMs conditioned on speech to maintain instruction-following capabilities for unseen tasks, such as speech translation and emotion recognition, even when trained exclusively on Automatic Speech Recognition (ASR) data. The framework utilizes a distance-based CTC loss over a frozen LLM embedding matrix, achieving strong performance on ASR tasks and generalizing effectively to new instructions without prior exposure. AI

IMPACT This framework could improve the generalization capabilities of speech-based language models, enabling them to handle a wider range of tasks beyond their initial training.

RANK_REASON The cluster describes a new research framework and its performance on specific tasks, detailed in a paper. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

DirectSpeech2LLM framework tackles prompt overfitting in Speech-LLMs

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The cluster describes a new research framework and its performance on specific tasks, detailed in a paper. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    DirectSpeech2LLM: A Simple End-to-End Framework to Mitigate Prompt Overfitting in Speech-LLMs

    Speech-LLMs often exhibit prompt overfitting, where models solely trained on automatic speech recognition (ASR) instruction fail to generalize to new instructions such as speech translation and continue to behave primarily as ASR system. We propose DirectSpeech2LLM, a simple end-…

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