3 papers
cs.CL2025
Synthetic Clarification and Correction Dialogues about Data-Centric Tasks -- A Teacher-Student Approach
Christian Poelitz, Nick McKenna
Real dialogues with AI assistants for solving data-centric tasks often follow dynamic, unpredictable paths due to imperfect information provided by the user or in the data, which m…
cs.CL2025
Synthetic Function Demonstrations Improve Generation in Low-Resource Programming Languages
Nick McKenna, Xinnuo Xu, Jack Williams +3
A key consideration when training an LLM is whether the target language is more or less resourced, for example English compared to Welsh, or Python compared to Excel. Typical train…
cs.AI2024
Evaluating the Evaluator: Measuring LLMs' Adherence to Task Evaluation Instructions
Bhuvanashree Murugadoss, Christian Poelitz, Ian Drosos +5
LLMs-as-a-judge is a recently popularized method which replaces human judgements in task evaluation (Zheng et al. 2024) with automatic evaluation using LLMs. Due to widespread use…