Health NLP Lab

🎤 2. Eugene Yang: Deep Research is Still All About Retrieval On September 17–18, we welcomed Dr. Eugene Yang, Senior Research Scientist at the Human Language Technology Center of Excellence at The Johns Hopkins University, to our lab for a talk on “Deep Research is Still All About Retrieval.”

🧠 3. Hiba Arnaout: Building Trustworthy NLP for Mental Health Care On September 22–23, we welcomed Dr. Hiba Arnaout, a postdoctoral researcher at Technische Universität Darmstadt, who presented her talk, “Building Trustworthy NLP for Mental Health Care.”

📍 1. Our first lab retreat In early September, we held our very first lab retreat. It was a fantastic opportunity to spend time together outside our usual routines, and we’re already looking forward to making it an annual tradition.

Our lab is growing, and today we’re excited to introduce Florentin Beck, one of our new PhD students! Florentin completed his Master’s in Medical Radiation Sciences on the AI track at the University of Tübingen, where he also began researching efficient language models through unstructured pruning.

His research focuses on multimodal foundation models for medicine, model efficiency, and trustworthy AI, with a particular focus on robust multimodal classification of epileptic seizure events and their clinical application.

With our entire US PhD cohort now graduated, today we are celebrating Dr. Catherine Chen and her successful PhD journey! 🎉

We are excited to see Catherine take the next step in her academic career as a postdoctoral researcher at Leiden University, where she will continue her research on how explanations affect trust in human-AI collaborative systems.

catherineschen.github.ioHomepage - Catherine ChenI am a researcher who recently defended my PhD at Brown University, where I worked with Carsten Eickhoff in the Health NLP Lab. Stay tuned for where I'm headed next!

Congratulations, Dr. Chen, on this achievement, and all the best for this exciting next chapter! 🎓 Follow Catherine's work here: 🔗 Personal Website: catherineschen.github.io 🔗 Google Scholar: scholar.google.com/citations?us... #FeatureFriday #PhDJourney #InformationRetrieval #ExplainableAI

With our entire US PhD cohort now graduated, today we are celebrating Dr. Catherine Chen and her successful PhD journey! 🎉

On June 15, 2026, Catherine successfully defended her PhD thesis, "Investigating Mechanisms of Relevance for Explainable Information Retrieval Systems", completing her doctoral research at Brown University.

Her work explored what makes information relevant and how to make retrieval systems more transparent and interpretable, contributing to more effective, understandable, and accountable AI.

health-nlp.comHealth NLP | ICLR 2026, Rio de Janeiro, Brazil© Health NLP Lab 2024. Design: HTML5 UP. Images: Unsplash.

17 Aug

If you missed #ICLR2026, here is a list of highlighted papers, chosen and recommended by Dr. Ali Bahrainian: 🔗 health-nlp.com/posts/iclr26

6 Aug

#ThrowbackThursday to #ACL2026 where Siran Li presented "MATCHA: Matching Text via Contrastive Semantic Alignment". Haven't read it yet? Here is a direct link to the paper: aclanthology.org/2026.finding...

20 July

Melbourne is the place to be this week! 🇦🇺 #SIGIR2026 has officially kicked off, and while we miss Carsten Eickhoff, Harrisen Scells, and Gregory Polyakov back in the office, the Health NLP Lab is well represented in Melbourne this week.

𝐇𝐮𝐦𝐚𝐧𝐬, 𝐋𝐋𝐌𝐬, 𝐚𝐧𝐝 𝐌𝐞𝐚𝐬𝐮𝐫𝐞𝐬 𝐃𝐨 𝐍𝐨𝐭 𝐀𝐥𝐢𝐠𝐧 𝐢𝐧 𝐀𝐭𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐝 𝐈𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥 ✒️Lukas Gienapp, Jenny Lang, Martin Potthast, Harrisen Scells dl.acm.org/doi/10.1145/...

arxiv.orgUnderstanding Wacky Weights: A Dissection of SPLADE's Learned Term ImportanceLearned sparse retrieval models such as SPLADE combine the effectiveness of neural architectures with the efficiency of inverted indices. As these models assign weights to terms from a fixed…

20 July

𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐖𝐚𝐜𝐤𝐲 𝐖𝐞𝐢𝐠𝐡𝐭𝐬: 𝐀 𝐃𝐢𝐬𝐬𝐞𝐜𝐭𝐢𝐨𝐧 𝐨𝐟 𝐒𝐏𝐋𝐀𝐃𝐄'𝐬 𝐋𝐞𝐚𝐫𝐧𝐞𝐝 𝐓𝐞𝐫𝐦 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐜𝐞 ✒️Gregory Polyakov, Harrisen Scells, Carsten Eickhoff arxiv.org/abs/2605.19628

Here are the #SIGIR2026 papers featuring our lab members: 𝐓𝐨𝐩𝐢𝐜-𝐒𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐞𝐫𝐬 𝐚𝐫𝐞 𝐁𝐞𝐭𝐭𝐞𝐫 𝐑𝐞𝐥𝐞𝐯𝐚𝐧𝐜𝐞 𝐉𝐮𝐝𝐠𝐞𝐬 𝐭𝐡𝐚𝐧 𝐏𝐫𝐨𝐦𝐩𝐭𝐞𝐝 𝐋𝐋𝐌𝐬 ✒️Lukas Gienapp, Martin Potthast, Andrew Yates, Harrisen Scells, Eugene Yang arxiv.org/abs/2510.04633

12 June

#FeatureFriday Today, we are celebrating a major achievement for Dr. Tassallah Amina Abdullahi! 🎉 On April 20, 2026, Tassallah successfully defended her PhD thesis, “Towards Trustworthy Clinical AI: Knowledge Grounding, Inference Reliability, and Behavioral Control.”

Tassallah has established a strong record of impactful publications and has been an active contributor to the AI research community. Her work sits at the intersection of machine learning, natural language processing, and clinical AI, bridging technical innovation with real-world healthcare needs.

tassabdul.github.ioTassallah Amina AbdullahiPhD student at Brown University

12 June

Explore her research: 🌐 tassabdul.github.io 📚 scholar.google.com/citations?us... Congratulations, Dr. Abdullahi, on this outstanding achievement. We are excited to see where your journey takes you next and the impact your work will continue to have on trustworthy AI and healthcare. 🚀

Her dissertation tackles one of the most important challenges facing modern AI: how do we ensure that systems deployed in healthcare are not only capable, but also trustworthy?

12 June

During her PhD, she explored questions about the reliability, reasoning, and controllability of AI systems in clinical environments. Her research contributed new insights into knowledge-grounded models, dependable inference, and mechanisms for steering AI behavior.

3 June

How can we reliably evaluate whether AI-generated text is actually correct? This question is becoming increasingly important as large language models are deployed in high-stakes domains, from healthcare and education to scientific research.

We evaluated MATCHA across eight public benchmarks spanning question answering, summarization, image captioning, natural language inference, and semantic textual similarity, consistently outperforming widely used evaluation approaches and demonstrating stronger alignment with human judgments.

3 June

As the field continues to push the boundaries of generative AI, improving how we evaluate model outputs is just as important as improving the models themselves. We hope MATCHA contributes toward more reliable, trustworthy, and semantically grounded evaluation of AI systems.

To address this gap, we present our #ACL2026 paper "MATCHA: Matching Text via Contrastive Semantic Alignment": arxiv.org/pdf/2605.27345

3 June

In this work, we introduce MATCHA, a novel evaluation metric that not only rewards agreement with a reference text but also explicitly penalizes contradictions. The key idea is that a good evaluation metric should recognize both what is correct and what is clearly wrong.

Yet many of today's most widely used evaluation metrics, such as ROUGE and BERTScore, cannot be relied on, since they can assign remarkably similar scores to statements that directly contradict one another.

One by one, our PhD students at Brown University are reaching graduation milestones, and today’s post is dedicated to Dr. Ruochen Zhang! 🎉

Through her work, Ruochen advanced research on multilingual NLP, cross-lingual transfer, and language representation in modern AI systems. She also built an impressive publication record and made impactful contributions to the NLP and machine learning community through insightful research.

ruochenzhang.comRuochen ZhangI am currently a PhD student in Computer Science at Brown University. I am fortunate to be advised by Professor Carsten Eickhoff and Professor Ellie Pavlick. Before my PhD, I received my master’s…

We are also excited that she will be joining Microsoft as an Applied Scientist for the next stage of her career. Follow her work on her personal website: buff.ly/BKia9Ca Congratulations again to Dr. Zhang, and we wish her all the best in this exciting next chapter! 🚀 #FeatureFriday #PhDJourney

One by one, our PhD students at Brown University are reaching graduation milestones, and today’s post is dedicated to Dr. Ruochen Zhang! 🎉

On April 15, 2026, Ruochen successfully defended her PhD thesis, "Understanding Multilingualism in Large Language Models", culminating years of impactful research on how large language models represent, transfer, and generalize knowledge across languages and linguistic settings.

#ThrowbackThursday to #ICLR2026, where Dr. Ali Bahreinian presented the paper "When Silence Is Golden: Can LLMs Learn to Abstain in Temporal QA and Beyond?" during the poster session.

The study demonstrates that teaching LLMs when not to answer can substantially improve both reasoning performance and reliability in temporal question answering tasks.

arxiv.orgWhen Silence Is Golden: Can LLMs Learn to Abstain in Temporal QA and Beyond?Large language models (LLMs) rarely admit uncertainty, often producing fluent but misleading answers, rather than abstaining (i.e., refusing to answer). This weakness is even evident in temporal…

Beyond achieving strong empirical results, the work offers valuable insights into how abstention and reasoning can be jointly optimized, paving the way toward more trustworthy and reliable LLMs. Don't miss the read: arxiv.org/abs/2602.04755

#ThrowbackThursday to #ICLR2026, where Dr. Ali Bahreinian presented the paper "When Silence Is Golden: Can LLMs Learn to Abstain in Temporal QA and Beyond?" during the poster session.

This work tackles one of the most pressing challenges in LLM reliability: models often generate confident yet misleading answers instead of admitting uncertainty or abstaining when sufficient evidence is unavailable.

Adopting a novel approach, the paper frames abstention as a teachable reasoning skill and introduces a training pipeline that combines Chain-of-Thought supervision with Reinforcement Learning guided by abstention-aware rewards.

24 Apr

Need a thought-provoking read for the weekend? Don’t miss this article by Prof. Carsten Eickhoff: “Half of AI health answers are wrong even though they sound convincing,” published in The Conversation EU.

theconversation.comHalf of AI health answers are wrong even though they sound convincing – new studyAI chatbots can sound authoritative on health, but new research shows they often mislead, especially when users must interpret and apply the answers themselves.

24 Apr

The piece highlights a striking finding: many AI-generated health responses are inaccurate or incomplete, despite sounding highly credible. It’s a timely reminder of both the potential and the risks of relying on AI in healthcare. Read it here: theconversation.com/half-of-ai-h...

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24 Apr

Six days to go! Apply by April 30 for the Director of our new AI Methods & Software Hub. This is a unique opportunity to build and lead a central hub at the intersection of cutting-edge ML research and scientific applications. More information: uni-tuebingen.de/en/128980#c2...

27 Mar

Great conference ahead! We are traveling to the Netherlands to attend #ECIR2026, and we are particularly excited about our tutorial on Mechanistic Interpretability tutorial on Sunday and the Collab-a-thons. Looking forward to insightful exchanges and constructive conversations.

youtu.beInterpretability Analysis of Arithmetic In-Context Learning in Large Language ModelsLarge language models (LLMs) exhibit sophisticated behavior, notably solving arithmetic with only a few in-context examples (ICEs). Yet the computations that connect those examples to the answer…

13 Mar

New video presentation available on YouTube! You can now watch Gregory Polyakov, the first author of "Interpretability Analysis of Arithmetic In-Context Learning in Large Language Models", explain their research question, methodology, and results. You can watch the video here:

5 Mar

New video alert! The video presentation of the paper "A Survey on LLM-Assisted Clinical Trial Recruitment" by Dr. Shrestha Ghosh is now live on YouTube. 🎞️ Check out the video here: www.youtube.com/watch?v=fE_3... And don't forget to subscribe to our YouTube channel for more videos!

27 Feb

Heartfelt congratulations to Dr. Michal Golovanevsky! 🎉 On January 30, 2026, our PhD student at Brown University successfully defended her thesis, "Advancing Attention Mechanisms in Multimodal Deep Learning Models", marking the culmination of years of research excellence and intellectual growth.

Michal has a rich portfolio of publications across top venues, and her most recent paper, "Mechanisms of Prompt-Induced Hallucination in Vision-Language Models", currently under review at ACL 2026, continues this trajectory of high-impact research.

27 Feb

With this milestone achieved, she will continue her research as a postdoctoral researcher starting in March 2026. You can explore her body of work here: dblp.org/pid/322/7058... Join us in celebrating Dr. Michal Golovanevsky for her persistence, creativity, and contributions to the field of CS👏

In her PhD years, she focused on understanding the internal workings of VLMs, with particular attention to scalability, interpretability, and control. Her work blends rigorous theory with practical insights, pushing forward how attention mechanisms are designed and understood in multimodal contexts.

18 Feb

Thank you to everyone who joined our invited talk last Friday. We were very happy to welcome Kay Brosien from x-cardiac GmbH and to learn from his practical insights on AI regulation in medicine.

18 Feb

Kay highlighted what it really means to translate research into clinical practice, outlined common regulatory pathways, and shared typical mistakes teams make when compliance is treated as an afterthought. The Q&A that followed showed how relevant these challenges are to many projects.