Rosni Vasu

selected publications

1. EMNLP

   HypER: Literature-grounded Hypothesis Generation and Distillation with Provenance

   Vasu, Rosni, Basu, Chandrayee,  Mishra, Bhavana Dalvi and 3 more authors

   2025

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   Large Language models have demonstrated promising performance in research ideation across scientific domains. Hypothesis development, the process of generating a highly specific declarative statement connecting a research idea with empirical validation, has received relatively less attention. Existing approaches trivially deploy retrieval augmentation and focus only on the quality of the final output ignoring the underlying reasoning process behind ideation. We present HypER (Hypothesis Generation with Explanation and Reasoning), a small language model (SLM) trained for literature-guided reasoning and evidence-based hypothesis generation. HypER is trained in a multi-task setting to discriminate between valid and invalid scientific reasoning chains in presence of controlled distractions. We find that HypER outperformes the base model, distinguishing valid from invalid reasoning chains (+22% average absolute F1), generates better evidence-grounded hypotheses (0.327 vs. 0.305 base model) with high feasibility and impact as judged by human experts (>3.5 on 5-point Likert scale).

2. HealTAC

   A Large Language Model based Framework for Dementia Related Hypothesis Generation

   Vasu, Rosni, Dong, Hang,  Abdulle, Yusuf and 2 more authors

   In Healthcare Text Analytics Conference Jun 2025

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3. ISWC

   SciHyp: A Fine-grained Dataset Describing Hypotheses and Their Components from Scientific Articles.

   Vasu, Rosni, Sarasua, Cristina,  and Bernstein, Abraham

   In International Semantic Web Conference Nov 2024

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   🏅Received Best Resource Paper Award Nomination🏅

4. AAAI

   Med-EASi: Finely Annotated Dataset and Models for Controllable Simplification of Medical Texts.

   Basu, Chandrayee,  Vasu, Rosni,  Yasunaga, Michihiro and 1 more author

   In Association for the Advancement of Artificial Intelligence, AAAI 2023 Feb 2023

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   Automatic medical text simplification can assist providers with patient-friendly communication and make medical texts more accessible, thereby improving health literacy. But curating a quality corpus for this task requires the supervision of medical experts. In this work, we present Med-EASi (Medical dataset for Elaborative and Abstractive Simplification), a uniquely crowdsourced and finely annotated dataset for supervised simplification of short medical texts. Its expert-layman-AI collaborative annotations facilitate controllability over text simplification by marking four kinds of textual transformations: elaboration, replacement, deletion, and insertion. To learn medical text simplification, we fine-tune T5-large with four different styles of input-output combinations, leading to two control-free and two controllable versions of the model. We add two types of controllability into text simplification, by using a multi-angle training approach: position-aware, which uses in-place annotated inputs and outputs, and position-agnostic, where the model only knows the contents to be edited, but not their positions. Our results show that our fine-grained annotations improve learning compared to the unannotated base- line. Furthermore, our position-aware control enhances the model’s ability to generate better simplification than the position-agnostic version. The data and code are available at https://github.com/Chandrayee/CTRL-SIMP.

5. HUMAN.AI@AAAI

   Automatic Medical Text Simplification: Challenges of Data Quality and Curation.

   Basu, Chandrayee,  Vasu, Rosni,  Yasunaga, Michihiro and 2 more authors

   In Human Partnership with Medical Artificial Intelligence (HUMAN.AI) ), AAAI Fall 2021 Symposium Nov 2021

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   Health Literacy is the degree to which individuals can comprehend basic health information needed to make appropriate health decisions. The topmost reason for low health literacy is the vocabulary gap between providers and patients. Automatic medical text simplification can contribute to improving health literacy by assisting providers with patientfriendly communication, improving health data search, and making online medical texts more accessible. It is, however, extremely challenging to curate quality corpus for this natural language processing (NLP) task. In this position paper, we observe that, despite recent research efforts, existing open corpora for medical text simplification are poor in quality and size. In order to match the progress in general text simplification and style transfer, we must leverage careful crowdsourcing. We discuss the challenges of naive crowd-sourcing. We propose that careful crowd-sourcing for medical text simplification is possible, when combined with automatic data labeling, a well-designed expert-layman collaboration framework, and context-dependent crowd-sourcing instructions.