With all the recent advances in the field of artificial intelligence, it is now possible to define intelligent systems with a clearer understanding of language than ever before. With each upgrade and release, the Large Language Model is becoming more capable of meeting the needs of different applications and scenarios. Having the right training prompts along with its design and content is critical for a robust and efficient model. Prompt engineering involves designing prompts that enable the user to receive appropriate responses from the model. Its main purpose is to supply high-quality training prompts to the model so that it can easily find patterns and trends in the data.
Prompt engineering research, particularly in the area of audio and speech processing, has received a lot of attention, but is relatively new compared to other areas. His Whisper models released by OpenAI are transformer-based encoder/decoder models that can be categorized into his two groups: English-only and multilingual. Whisper is an automatic speech recognition model trained on a large dataset of 680,000 hours of web-scraped speech data.
In a recently published research paper, a team of researchers discussed adapting the Whisper model to unseen tasks using simple prompts. The main approach of a researcher called PromptingWhisper was to investigate its zero-shot task generalization ability by analyzing the strengths and weaknesses of the Whisper model. To adapt Whisper to invisible tasks, the team used prompt engineering to design task-specific prompts. They mainly discussed his three specific tasks: audiovisual speech recognition (AVSR), code-switching speech recognition (CS-ASR), and speech translation (ST) involving unseen language pairs.
In AVSR, the team found that Whisper exhibited robust behavior in terms of length and noisiness of visual prompts. The efficiency of visual prompts in the English model is different compared to the multilingual model. We found some performance gaps between different accents in CS-ASR. Finally, we found that ST can effectively use task tokens in prompts to tell the model to perform transformations. To customize prompts to the specific requirements of each task, the team either manipulated special tokens within the default prompts provided by Whisper or used another large-scale model.
The team conducted experiments to evaluate the performance of the Whisper model. Comparing the default prompts with the proposed task-specific prompts, the prompts found that he significantly improved performance across the three zero-shot tasks, with performance gains ranging from 10% to 45%. rice field. In some cases, the suggested prompts even outperformed her SOTA supervised model on certain datasets.
In conclusion, the researchers thoroughly investigated the Whisper model. During the evaluation, they demonstrated how his Whisper is robust to a variety of prompts, how efficiently it reveals accent-related biases, and how the model understands multiple languages within the latent space. Ability to identify or observed. They focused on the gradient-free zero-shot task generalization ability of web-scale speech models, and studied and analyzed the hidden strengths and weaknesses of Whisper in detail.
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Tanya Malhotra is a final year student at the University of Petroleum and Energy Research, Dehradun, graduating with a Bachelor of Science in Computer Science Engineering with a specialization in Artificial Intelligence and Machine Learning.
A data science enthusiast with good analytical and critical thinking, she has a keen interest in learning new skills, leading groups, and managing work in an organized manner.
