Flash Forward is a show about possible (and not so possible) future scenarios. What would the warranty on a sex robot look like? How would diplomacy work if we couldn’t lie? Could there ever be a fecal transplant black market? (Complicated, it wouldn’t, and yes, respectively, in case you’re curious.) Hosted and produced by award winning science journalist Rose Eveleth, each episode combines audio drama and journalism to go deep on potential tomorrows, and uncovers what those futures might re ...
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126 - Optimizing Continuous Prompts for Generation, with Lisa Li
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Content provided by NLP Highlights and Allen Institute for Artificial Intelligence. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by NLP Highlights and Allen Institute for Artificial Intelligence or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
We invited Lisa Li to talk about her recent work, Prefix-Tuning: Optimizing Continuous Prompts for Generation. Prefix tuning is a lightweight alternative to finetuning, and the idea is to tune only a fixed-length task-specific continuous vector, and to keep the pretrained transformer parameters frozen. We discussed how prefix tuning compares with finetuning and other efficient alternatives on two tasks in various experimental settings, and in what scenarios prefix tuning is preferable. Lisa is a Phd student at Stanford University. Lisa's webpage: https://xiangli1999.github.io/ The hosts for this episode are Pradeep Dasigi and Ana Marasović.
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145 episodes
MP3•Episode home
Manage episode 293241805 series 1452120
Content provided by NLP Highlights and Allen Institute for Artificial Intelligence. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by NLP Highlights and Allen Institute for Artificial Intelligence or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
We invited Lisa Li to talk about her recent work, Prefix-Tuning: Optimizing Continuous Prompts for Generation. Prefix tuning is a lightweight alternative to finetuning, and the idea is to tune only a fixed-length task-specific continuous vector, and to keep the pretrained transformer parameters frozen. We discussed how prefix tuning compares with finetuning and other efficient alternatives on two tasks in various experimental settings, and in what scenarios prefix tuning is preferable. Lisa is a Phd student at Stanford University. Lisa's webpage: https://xiangli1999.github.io/ The hosts for this episode are Pradeep Dasigi and Ana Marasović.
…
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145 episodes
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