Prompting the Hidden Talent of Web-Scale Speech Models for Zero-Shot Task Generalization (2305.11095v3)

Abstract: We investigate the emergent abilities of the recently proposed web-scale speech model Whisper, by adapting it to unseen tasks with prompt engineering. We selected three tasks: audio-visual speech recognition (AVSR), code-switched speech recognition (CS-ASR), and speech translation (ST) on unseen language pairs. We design task-specific prompts, by either leveraging another large-scale model, or simply manipulating the special tokens in the default prompts. Experiments show that compared to the default prompts, our proposed prompts improve performance by 10% to 45% on the three zero-shot tasks, and even outperform SotA supervised models on some datasets. In addition, our experiments reveal many interesting properties of Whisper, including its robustness to prompts, bias on accents, and the multilingual understanding in its latent space. Code is available at https://github.com/jasonppy/PromptingWhisper

• The paper presents a prompt engineering approach that adapts the Whisper model to novel tasks, achieving performance gains from 9% to 45% over default settings.
• It details innovative modifications for AVSR, CS-ASR, and ST, showing improved error rates and effective multilingual speech processing without model retraining.
• The study highlights the potential of zero-shot prompt modifications to reveal latent multilingual and multimodal abilities in large-scale speech models.

Insights into the Paper "Prompting the Hidden Talent of Web-Scale Speech Models for Zero-Shot Task Generalization"

The research presented in the paper addresses the challenges of adapting web-scale speech models to perform tasks they were not initially trained for, specifically using a prompt-based methodology. This paper focuses on leveraging the emergent capabilities of the Whisper model, a prominent web-scale speech model, by employing prompt engineering for zero-shot task generalization. The paper's objective is to assess whether adjusting the prompts can guide the model to perform well on unseen tasks, such as audio-visual speech recognition (AVSR), code-switched speech recognition (CS-ASR), and speech translation (ST) on previously unencountered language pairs.

Summary of Key Findings

1. Prompt Engineering for Task Adaptation:
   • The research explores the modification of Whisper's default prompts, either by incorporating vision-and-LLM outputs or by altering special tokens. This approach demonstrates notable improvements, with performance gains ranging from 9% to 45% compared to the default prompts across the selected tasks.
2. Task-Specific Adaptations:
   • Audio-Visual Speech Recognition (AVSR): By integrating CLIP-oriented visual prompts, the model demonstrates improved word error rates (WER), outperforming some state-of-the-art AVSR systems.
   • Code-Switched Speech Recognition (CS-ASR): Introducing dual language tokens within the prompt enhances recognition accuracy on code-switched corpora, namely, Mandarin-English datasets, indicating the potential to handle linguistic diversity without explicit model retraining.
   • Speech Translation (ST): Utilizing task tokens designated originally for ASR, instead of translation-specific tokens, Whisper showcases emergent En→X translation capabilities, yielding outputs that closely align with supervised translation systems.
3. Robustness and Hidden Capabilities:
   • The experiments reveal Whisper's robustness to variations in prompt lengths and noise. Moreover, the model exhibits unexpected behavior such as performing translations it wasn't explicitly trained to do, hinting at latent multilingual understanding within its architecture.

Implications and Speculations

The paper primarily signifies that sophisticated prompt engineering can sufficiently direct the capabilities of large-scale models to perform diverse, previously unseen tasks without altering their foundational architectures. This insight could have significant implications in practice, where the fine-tuning of large models can be resource-intensive. Additionally, the model's promising performance in handling code-switched and multilingual data without direct supervision places emphasis on the potential utility in multilingual and multicultural speech processing environments.

Furthermore, the findings inspire future research directions, such as investigating the latent structure of multilingual representations and exploring prompt adaptations in other large-scale models for various unseen tasks. A deeper understanding of these models' latent task performances can drive the development of more versatile AI systems capable of broader contextual understanding and task execution.

In conclusion, the paper delineates a methodology with considerable implications for deploying large-scale speech models in versatile environments, illustrating that careful prompt alterations can unveil and utilize hidden talents within these models, facilitating groundbreaking applications spanning across languages and modalities.