Prefix-Tuning: Optimizing Continuous Prompts for Generation

When:
Thursday, June 17, 2021, 11:00 am - 12:00 pm PDTiCal
Where:
https://usc.zoom.us/j/92263295642This event is open to the public.
Type:
NL Seminar
Speaker:
Xiang Lisa Li (Stanford University)
Video Recording:
TBA
Description:

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Abstract: 
Fine-tuning is the de facto way of leveraging large pretrained language models for downstream tasks. However, fine-tuning modifies all the language model parameters and therefore necessitates storing a full copy for each task. In this paper, we propose prefix-tuning, a lightweight alternative to fine-tuning for natural language generation tasks, which keeps language model parameters frozen and instead optimizes a sequence of continuous task-specific vectors, which we call the prefix. Prefix-tuning draws inspiration from prompting for language models, allowing subsequent tokens to attend to this prefix as if it were “virtual tokens”. We apply prefix-tuning to GPT-2 for table-to-text generation and to BART for summarization. We show that by modifying only 0.1% of the parameters, prefix-tuning obtains comparable performance in the full data setting, outperforms fine-tuning in low-data settings, and extrapolates better to examples with topics that are unseen during training. 

Bio: Xiang Lisa Li is a first-year PhD student in computer science at Stanford University, advised by Percy Liang and Tatsunori Hashimoto. She works on controllable text generation/decoding and efficient adaptation of pre-trained language models. Lisa is supported by a Stanford Graduate Fellowship and is the recipient of an EMNLP Best Paper award.

Recording approved by Speaker

The recording will be posted on USC/ISI's YouTube site within 1-2 business days from the date of the presentation. https://www.youtube.com/user/USCISI

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