Papers by Steven R Wilson
How Hard is Math? Using Quantitative Metrics to Measure LLM Alignment to Human Intuitions of Difficulty (2026.acl-srw)
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| Challenge: | Often overlooked is how "difficulty" is operationalized in the context of LLM problem solving tasks. |
| Approach: | They examine quantitative measurements from LLM-generated solutions and evaluate their inter-correlations with human-annotated difficulty scores. |
| Outcome: | The proposed model shows that LLMs can solve problems with reasonable accuracy, but performance is poor when generalizing to other benchmarks. |
Representing and Clustering Errors in Offensive Language Detection (2025.naacl-srw)
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| Challenge: | Sentence-BERT embeddings of Large Language Model (LLM)-generated linguistic features give the most interpretable clustering for Arabic errors. |
| Approach: | They evaluate the K-Means clustering of four text representations for the task of offensive language detection in English and Levantine Arabic. |
| Outcome: | The proposed clustering of four text representations for offensive language detection in English and Levantine Arabic gives the most human-interpretable clustering for English errors and the grouping is mainly based on the targeted group in the text. |
TounsiBench: Benchmarking Large Language Models for Tunisian Arabic (2025.emnlp-main)
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| Challenge: | a dataset of Tunisian Arabic instructions and prompts is used to evaluate LLMs' ability to understand and generate responses in Tunisia . we assess the quality, correctness, relevance, and dialectal adherence of LLM responses . |
| Approach: | They propose a benchmark for evaluating the capabilities of large language models in Tunisian Arabic . they use a dataset of Tunisia Arabic instructions and prompts to evaluate their models . |
| Outcome: | The proposed model can judge quality, correctness, relevance, and dialectal adherence . the model can also generate a leaderboard for the Tunisian Arabic language . |
Social Story Frames: Contextual Reasoning about Narrative Intent and Reception (2026.acl-long)
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Joel Mire, Maria Antoniak, Steven R Wilson, Zexin Ma, Achyutarama R Ganti, Andrew Piper, Maarten Sap
| Challenge: | SocialStoryFrames is a formalism for distilling plausible inferences about reader response . authors characterize frequency and interdependence of storytelling intents across communities . |
| Approach: | They propose a formalism for distilling plausible inferences about reader response using conversational context and a taxonomy grounded in narrative theory, linguistic pragmatics, and psychology. |
| Outcome: | The proposed model can be used to analyze reader responses in online communities. |
NLP for Social Good: A Survey and Outlook of Challenges, Opportunities and Responsible Deployment (2026.eacl-long)
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Antonia Karamolegkou, Angana Borah, Eunjung Cho, Sagnik Ray Choudhury, Martina Galletti, Pranav Gupta, Oana Ignat, Priyanka Kargupta, Neema Kotonya, Hemank Lamba, Sun-Joo Lee, Arushi Mangla, Ishani Mondal, Fatima Zahra Moudakir, Deniz Nazar, Poli Nemkova, Dina Pisarevskaya, Naquee Rizwan, Nazanin Sabri, Keenan Samway, Dominik Stammbach, Anna Steinberg Schulten, David Tomás, Steven R Wilson, Bowen Yi, Jessica H Zhu, Arkaitz Zubiaga, Anders Søgaard, Alexander Fraser, Zhijing Jin, Rada Mihalcea, Joel R. Tetreault, Daryna Dementieva
| Challenge: | This paper surveys work in "NLP for Social Good" across nine domains relevant to global development and risk agendas. |
| Approach: | This paper analyzes work in "NLP for Social Good" across nine domains relevant to global development and risk agendas. |
| Outcome: | The paper analyzes work in "NLP for Social Good" across nine domains relevant to global development and risk agendas. |