Scholar
Sarah Wiegreffe
Google Scholar ID: YoR3IugAAAAJ
University of Maryland
natural language processing
machine learning
interpretability
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Citations & Impact
All-time
Citations
5,560
H-index
14
i10-index
16
Publications
20
Co-authors
26
list available
Contact
Twitter
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Publications
6 items
What Drives Representation Steering? A Mechanistic Case Study on Steering Refusal
2026
Cited
0
Are Latent Reasoning Models Easily Interpretable?
2026
Cited
0
Quantifying the Gap between Understanding and Generation within Unified Multimodal Models
2026
Cited
0
Everything is Plausible: Investigating the Impact of LLM Rationales on Human Notions of Plausibility
2025
Cited
0
On Linear Representations and Pretraining Data Frequency in Language Models
2025
Cited
0
Answer, Assemble, Ace: Understanding How LMs Answer Multiple Choice Questions
2024
Cited
0
Resume (English only)
Academic Achievements
2025: Mechanistic Interpretability Benchmark (MIB) accepted to ICML 2025; organizing the Actionable Interpretability Workshop at ICML
2025: Two papers accepted to ICLR 2025, one as a spotlight
2024: Selected as a Rising Star in Machine Learning
2024: Selected as a Rising Star in Generative AI
2024: Paper on taxonomy for model noncompliance accepted to NeurIPS 2024 Datasets and Benchmarks
2024: Recognized as an Outstanding Area Chair at EMNLP 2024
2023: Self-Refine published at NeurIPS
2023: Outstanding Area Chair award at ACL 2023 (top 1.5%)
2023: Top reviewer at NeurIPS 2023
2023: Selected as a Rising Star in EECS
Multiple publications and invited talks at top conferences including ACL, EMNLP, NAACL, COLM, ICLR, and NeurIPS
2024: Co-taught a tutorial on 'Explanation in the Era of Large Language Models' at NAACL 2024
Active organizer and participant in workshops such as BlackBoxNLP on interpretability
Co-authors
26 total
Peter Clark
Allen Institute for Artificial Intelligence (AI2)
Co-author 2
Yuval Pinter
Ben-Gurion University of the Negev
Ana Marasović
University of Utah
Jacob Eisenstein
Google Research
Noah A. Smith
University of Washington; Allen Institute for Artificial Intelligence
Mark Riedl
Professor of Computing, Georgia Institute of Technology
Yejin Choi
Stanford University / NVIDIA
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