Talk Info: Xiang Lisa Li

Prefix-Tuning: Optimizing Continuous Prompts for Generation

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. I will introduce prefix-tuning, a lightweight alternative to fine-tuning for natural language generation tasks, which keeps language model parameters frozen and instead optimizes a small continuous task-specific vector, 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 find that by learning 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. Then I will discuss some downsides of lightweight fine-tuning (e.g., prefixtuning, adapters): they sometimes underperform full finetuning in-distribution (ID) on harder tasks. I will present methods to combine the benefits of full and lightweight finetuning, achieving strong performance both ID and OOD (out-of-distribution).

Bio:

Xiang Lisa Li is a second-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.

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