3 papers
cs.CL2025
Optimizing Decomposition for Optimal Claim Verification
Yining Lu, Noah Ziems, Hy Dang +1
Current research on the \textit{Decompose-Then-Verify} paradigm for evaluating the factuality of long-form text typically treats decomposition and verification in isolation, overlo…
cs.HC2024
Limitations of the LLM-as-a-Judge Approach for Evaluating LLM Outputs in Expert Knowledge Tasks
Annalisa Szymanski, Noah Ziems, Heather A. Eicher-Miller +3
The potential of using Large Language Models (LLMs) themselves to evaluate LLM outputs offers a promising method for assessing model performance across various contexts. Previous r…
cs.CL2024
TOWER: Tree Organized Weighting for Evaluating Complex Instructions
Noah Ziems, Zhihan Zhang, Meng Jiang
Evaluating the ability of large language models (LLMs) to follow complex human-written instructions is essential for their deployment in real-world applications. While benchmarks l…