Search Results for author: Jordan Lee Boyd-Graber

Found 3 papers, 3 papers with code

PANDA (Pedantic ANswer-correctness Determination and Adjudication):Improving Automatic Evaluation for Question Answering and Text Generation

1 code implementation17 Feb 2024 Zongxia Li, Ishani Mondal, Yijun Liang, Huy Nghiem, Jordan Lee Boyd-Graber

Question answering (QA) can only make progress if we know if an answer is correct, but for many of the most challenging and interesting QA examples, current answer correctness (AC) metrics do not align with human judgments, particularly verbose, free form answers from large language models (LLM).

Question Answering Text Generation

Is Automated Topic Model Evaluation Broken? The Incoherence of Coherence

1 code implementation NeurIPS 2021 Alexander Hoyle, Pranav Goel, Andrew Hian-Cheong, Denis Peskov, Jordan Lee Boyd-Graber, Philip Resnik

To address the standardization gap, we systematically evaluate a dominant classical model and two state-of-the-art neural models on two commonly used datasets.

Topic Models

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