Papers by Connor Baumler
Recognition of They/Them as Singular Personal Pronouns in Coreference Resolution (2022.naacl-main)
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| Challenge: | a new benchmark evaluates coreference resolution systems' ability to recognize singular personal "they" we find that current systems overwhelmingly choose to resolve "they's" correctly to a singular entity or to 'a group' |
| Approach: | They propose to evaluate coreference resolution systems for singular personal "they" they use WinoNB schemas to evaluate whether they can correctly resolve singular "they". |
| Outcome: | The proposed benchmark evaluates coreference resolution systems for singular personal "they" they show that they are biased toward resolving "they", not "them" |
Can You Make It Sound Like You? Post-Editing LLM-Generated Text for Personal Style (2026.acl-long)
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| Challenge: | Despite the growing use of large language models for writing tasks, it remains unclear whether users can effectively reshape LLM-generated text to reflect their personal style. |
| Approach: | They conduct an online study in which participants post-edit LLM-generated drafts for writing tasks where personal style matters to them. |
| Outcome: | The results show that post-editing increases stylistic similarity to unassisted writing and reduces similarity with fully LLM-generated output. |
Which Examples Should be Multiply Annotated? Active Learning When Annotators May Disagree (2023.findings-acl)
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| Challenge: | Disagreement in annotations is natural for humans, depending on background, identity, positionality . many active learning approaches focus on examples where model entropy and annotator entropicy are the most different. |
| Approach: | They propose an active learning approach that focuses annotations on examples where model entropy and annotator entropic are the most different. |
| Outcome: | The proposed approach reduces the number of annotations required by 24% on average across datasets. |
On the Mutual Influence of Gender and Occupation in LLM Representations (2025.acl-long)
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| Challenge: | We examine LLM representations of gender for first names in various occupational contexts to study how occupations and the gender perception of first names influence each other mutually. |
| Approach: | They examine LLM representations of gender for first names in various occupational contexts and examine how occupations and the gender perception of first names influence each other mutually. |
| Outcome: | The representations shift with the occupational context and are influenced by stereotypically feminine or masculine occupations. |
Hybrid Semantics for Goal-Directed Natural Language Generation (2022.acl-long)
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| Challenge: | Existing goal-directed natural language generation systems use first-order logic to represent semantics, but they are often slow due to the semantics of the partially realized text being checked. |
| Approach: | They propose to use logical semantics and distributional semantics to combine meaning representations to scale a goal-directed natural language generation system without losing expressiveness. |
| Outcome: | The proposed approach scales significantly better than the goal-directed generation system, but it is slower because the representations are not as precise as pure logical semantics. |
What Else Do I Need to Know? The Effect of Background Information on Users’ Reliance on QA Systems (2023.emnlp-main)
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Navita Goyal, Eleftheria Briakou, Amanda Liu, Connor Baumler, Claire Bonial, Jeffrey Micher, Clare Voss, Marine Carpuat, Hal Daumé III
| Challenge: | Existing NLP systems can only access the retrieved context to determine the answer, resulting in a knowledge gap between the information that is required to answer the question and the information available to assess the model’s correctness. |
| Approach: | They ask whether adding relevant background helps mitigate users’ over-reliance on predictions. |
| Outcome: | The proposed approach reduces over-reliance on model predictions even in the absence of sufficient information to assess their correctness. |
Who’s the Author? How Explanations Impact User Reliance in AI-Assisted Authorship Attribution (2025.findings-emnlp)
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| Challenge: | despite growing interest in explainable NLP, it remains unclear how explanation strategies shape user behavior in tasks like authorship identification. |
| Approach: | They propose two explanation types to support their analysis of user behavior . they use example-based style rewrites and feature-based rationales to generate explanations . |
| Outcome: | The proposed explanations support appropriate reliance, whereas explanations increase AI overreliance, the study finds . |