16 citations · 23 across the 5 of their papers we have counts for
5 papers
Learning from Less: Measuring the Effectiveness of RLVR in Low Data and Compute Regimes
Justin Bauer, Thomas Walshe, Derek Pham +4
Fine-tuning Large Language Models (LLMs) typically relies on large quantities of high-quality annotated data, or questions with well-defined ground truth answers in the case of Rei…
The Hitchhikers Guide to Rubric Quality Understanding and Enrichment
Zhengyang Qi, Ankit Aich, Charles Dickens +7
Rubrics distill notions of expert quality and measure agent performance. However, the quality of rubrics themselves have not been systematically measured and are often left to down…
Automating Benchmark Design
Amanda Dsouza, Harit Vishwakarma, Zhengyang Qi +6
The rapid progress and widespread deployment of LLMs and LLM-powered agents has outpaced our ability to evaluate them. Hand-crafted, static benchmarks are the primary tool for asse…
Using Chatbots to Teach Languages
Yu Li, Chun-Yen Chen, Dian Yu +6
This paper reports on progress towards building an online language learning tool to provide learners with conversational experience by using dialog systems as conversation practice…
ErAConD : Error Annotated Conversational Dialog Dataset for Grammatical Error Correction
Xun Yuan, Derek Pham, Sam Davidson +1
Currently available grammatical error correction (GEC) datasets are compiled using well-formed written text, limiting the applicability of these datasets to other domains such as i…