9 citations · 11 across the 3 of their papers we have counts for
3 papers
cs.CY2026
Consistently Good vs. Occasionally Great: A Rubric for Open-Ended Feedback Quality from Humans and Machines
Binglin Chen, Rajarshi Haldar, Max Fowler +2
Providing high-quality feedback on student work is essential for learning, yet delivering such feedback at scale remains challenging. In this paper, we focus on feedback for open-e…
cs.CL2025★ 9 cited
Rating Roulette: Self-Inconsistency in LLM-As-A-Judge Frameworks
Rajarshi Haldar, Julia Hockenmaier
As Natural Language Generation (NLG) continues to be widely adopted, properly assessing it has become quite difficult. Lately, using large language models (LLMs) for evaluating the…
cs.SE2024★ 2 cited
Analyzing the Performance of Large Language Models on Code Summarization
Rajarshi Haldar, Julia Hockenmaier
Large language models (LLMs) such as Llama 2 perform very well on tasks that involve both natural language and source code, particularly code summarization and code generation. We…