3 papers
cs.SE2026
PyMETA: A Benchmark Dataset for Hierarchical Student Code Error Classification with Python-Interpreter-Based Labels
Chuyue Li, Ziqi Tang, Jingyi Wang +3
With the advancement of Large Language Models (LLMs), code error detection has extended beyond traditional IDE diagnostics to context-sensitive debugging in educational scenarios.…
cs.CL2025
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…
cs.CL2023
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…