Papers by Alyssa Hwang
AMPERSAND: Argument Mining for PERSuAsive oNline Discussions (D19-1)
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| Challenge: | Argument mining is a field of corpus-based discourse analysis that involves the automatic identification of argumentative structures in text. |
| Approach: | They propose a computational model for argument mining in online persuasive discussion forums that brings together the micro-level (argument as product) and macro-level models of argumentation. |
| Outcome: | The proposed model improves on existing models using pointer networks and a pre-trained language model. |
FanOutQA: A Multi-Hop, Multi-Document Question Answering Benchmark for Large Language Models (2024.acl-short)
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| Challenge: | Existing benchmarks for large language models focus on intradocument dependencies or dependencies between a small number of documents. |
| Approach: | They propose to use a dataset of fan-out question-answer pairs and human-annotated decompositions with English Wikipedia as the knowledge base to evaluate models' reasoning. |
| Outcome: | The proposed dataset shows that models still have room to improve reasoning over inter-document dependencies in a long context. |
RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectors (2024.acl-long)
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Liam Dugan, Alyssa Hwang, Filip Trhlík, Andrew Zhu, Josh Magnus Ludan, Hainiu Xu, Daphne Ippolito, Chris Callison-Burch
| Challenge: | Existing methods for detecting machine-generated text are often insufficiently robust and lack benchmark datasets. |
| Approach: | They evaluate the out-of-domain and adversarial robustness of 8 open- and 4 closed-source detectors using RAID benchmark datasets. |
| Outcome: | The proposed detectors are fooled by adversarial attacks, repetition penalties, and unseen generative models. |