Papers by Jennifer Healey

5 papers
TextLap: Customizing Language Models for Text-to-Layout Planning (2024.findings-emnlp)

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Challenge: Creating 2D graphical layouts from text alone is challenging in traditional settings.
Approach: They propose to customize LLMs to allow users to generate professional looking layouts by simply inputting text instructions.
Outcome: The proposed method outperforms existing benchmarks for document generation and graphical design benchmarks.
LinkNav: Surfacing Interconnected Information in Scientific Articles (2026.acl-demo)

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Challenge: a non-linear reading order of academic literature is recognized by authors who make explicit connections between non-adjacent passages.
Approach: They propose an enhanced reading experience which generates questions and searches for answer-bearing passages in academic papers to form intra-document connections when answers are found.
Outcome: The proposed interface makes connections between related but non-adjacent passages even if the author did not make them explicit.
Principled Content Selection to Generate Diverse and Personalized Multi-Document Summaries (2025.acl-long)

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Challenge: Large language models exhibit the _”lost in the middle” phenomenon when they are unevenly attending to different parts of the provided context.
Approach: They propose principled content selection as a way to increase source coverage . they use determinantal point processes to prioritize diverse content .
Outcome: The proposed method improves source coverage on the DiverseSumm benchmark.
“It doesn’t look good for a date”: Transforming Critiques into Preferences for Conversational Recommendation Systems (2021.emnlp-main)

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Challenge: Conversational recommendation systems (CRSs) aim to refine options over multiple turns of a conversation, but they are not as flexible as real conversations.
Approach: They propose a method for transforming a user critique into a positive preference . they use a large neural language model to perform critique-to-preference transformation .
Outcome: The proposed method improves recommendations in restaurant domain using a new dataset of restaurant critiques.
A Flash in the Pan: Better Prompting Strategies to Deploy Out-of-the-Box LLMs as Conversational Recommendation Systems (2025.coling-main)

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Challenge: Recent studies have shown that using conversation history can improve question generation and product recommendation in naturalistic, multi-round conversational recommendation settings.
Approach: They propose a method to generate better questions to elicit human preferences and to make recommendations using the information gained through these questions.
Outcome: The proposed method beats state-of-the-art benchmarks on two datasets and shows that it is more accurate when users answer more questions than prior methods.

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