Papers by Jeremiah Milbauer

5 papers
LAIT: Efficient Multi-Segment Encoding in Transformers with Layer-Adjustable Interaction (2023.acl-long)

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Challenge: In many NLP tasks, the input text can be seen as a sequence of related segments.
Approach: They propose a layer-adjustable interactions framework that contextualizes token representations by attending to all other tokens at each layer, leading to quadratic increase in compute effort with the input length.
Outcome: The proposed model reduces 30-50% of attention FLOPs while maintaining high accuracy.
Beyond Text: Characterizing Domain Expert Needs in Document Research (2025.findings-acl)

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Challenge: Document research is a key part of almost all knowledge work, but are text-based NLP systems able to model these tasks as experts conceptualize and perform them?
Approach: They interview 16 domain experts to understand their processes of document research . they find that processes are idiosyncratic, iterative, and rely heavily on social context .
Outcome: The findings show that document research processes are idiosyncratic, iterative, and rely heavily on the social context of a document in addition to its content.
Stereotype or Personalization? User Identity Biases Chatbot Recommendations (2025.findings-acl)

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Challenge: We show that when people use large language models to generate recommendations, the LLMs produce responses that reflect both what the user wants and who the user is.
Approach: They propose that chatbots should transparently indicate when user’s revealed identity influences model recommendations but fail to do so .
Outcome: The proposed model generates racially stereotypical recommendations regardless of whether the user revealed their identity intentionally or unintentionally through implicit cues.
Aligning Multidimensional Worldviews and Discovering Ideological Differences (2021.emnlp-main)

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Challenge: Existing work on understanding worldviews and ideological distinctions focuses on political polarization . et al., 2018: a novel method for uncovering complex ideological and worldview characteristics of communities.
Approach: They propose a method to uncover multifaceted ideological differences across multiple axes . they use comments from the largest communities on reddit.com to train word embedding models .
Outcome: The proposed method can uncover complex ideological differences across multiple axes of polarization using over 1B comments from the largest communities on reddit.com representing 40% of Reddit activity.
NewsSense: Reference-free Verification via Cross-document Comparison (2023.emnlp-demo)

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Challenge: Various solutions have been proposed to assist with news reading, including media bias charts, which treat each news source as a whole, without digging into specific articles or topics.
Approach: They propose a novel sensemaking tool to integrate news articles on a central topic.
Outcome: The novel reading interface can help users identify key information, verify the credibility of news articles, explore different perspectives, and understand what content is supported, contradicted, or missing.

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