Seminars and Events

ISI Natural Language Seminar

NL Seminar-Informative Example Selection for In-Context Learning

Event Details

Speaker: Shivanshu Gupta, University of California, Irvine

Conference Rm Location: ISI-MDR #689 in-person attendance will be permitted for USC/ISI faculty, staff, students only. Open to the public virtually via Zoom

REMINDER:

Meeting hosts only admit guests that they know to the Zoom meeting. Hence, you’re highly encouraged to use your USC account to sign into Zoom.

If you’re an outside visitor, please inform us at (nlg-seminar-host(at)isi.edu) beforehand so we’ll be aware of your attendance and let you in.

In-person attendance will be permitted for USC/ISI faculty, staff, students only. Open to the public virtually via the zoom link.

For more information on the NL Seminar series and upcoming talks, please visit:

https://nlg.isi.edu/nl-seminar/

In-context Learning (ICL) uses large language models (LLMs) for new tasks by conditioning them on prompts comprising a few task examples. With the rise of LLMs that are intractable to train or hidden behind APIs, the importance of such a training-free interface cannot be overstated. However, ICL is known to be critically sensitive to the choice of in-context examples. Despite this, the standard approach for selecting in-context examples remains to use general-purpose retrievers due to the limited effectiveness and training requirements of prior approaches. In this talk, I’ll posit that good in-context examples demonstrate the salient information necessary to solve a given test input. I’ll present efficient approaches for selecting such examples, with a special focus on preserving the training-free ICL pipeline. Through results with a wide range of tasks and LLMs, I’ll demonstrate that selecting informative examples can indeed yield superior ICL performance.

Speaker Bio

Shivanshu Gupta is a Computer Science Ph.D. Candidate at the University of California Irvine, advised by Sameer Singh. Prior to this, he was a Research Fellow at LinkedIn and Microsoft Research India, and completed his B.Tech. and M.Tech. in Computer Science at IIT Delhi. His primary research interests are systematic generalization, in-context learning, and multi-step reasoning capabilities of large language models.

If speaker approves to be recorded for this NL Seminar talk, it will be posted on the USC/ISI YouTube page within 1-2 business days: https://www.youtube.com/user/USCISI.

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