2 citations · 2 across the 4 of their papers we have counts for
4 papers
PyMETA: Evaluating Student Code Diagnosis on and Beyond the First Execution Error
Chuyue Li, Ziqi Tang, Jingyi Wang +3
Large language models can diagnose a student program from its code, problem statement, and reference solution. Evaluating this ability requires a clear definition of what counts as…
Refract ICL: Rethinking Example Selection in the Era of Million-Token Models
Arjun R. Akula, Kazuma Hashimoto, Krishna Srinivasan +3
The emergence of long-context large language models (LLMs) has enabled the use of hundreds, or even thousands, of demonstrations for in-context learning (ICL) - a previously imprac…
Take One Step at a Time to Know Incremental Utility of Demonstration: An Analysis on Reranking for Few-Shot In-Context Learning
Kazuma Hashimoto, Karthik Raman, Michael Bendersky
In-Context Learning (ICL) is an emergent capability of Large Language Models (LLMs). Only a few demonstrations enable LLMs to be used as blackbox for new tasks. Previous studies ha…
Ambiguity-Aware In-Context Learning with Large Language Models
Lingyu Gao, Aditi Chaudhary, Krishna Srinivasan +3
In-context learning (ICL) i.e. showing LLMs only a few task-specific demonstrations has led to downstream gains with no task-specific fine-tuning required. However, LLMs are sensit…