Latest News

We are excited to share that our paper LogosKG: Hardware-Optimized Scalable and Interpretable Knowledge Graph Retrieval has been accepted to the main conference at ACL 2026. This work is led by He Cheng, PhD, postdoc researcher in our lab.

We’re thrilled to share that two of our papers were accepted and presented at EMNLP 2025, held this year in Suzhou, China: one in the Main Conference and one in the Findings of EMNLP.

We are delighted to announce that our paper, “Brittleness and Promise: Knowledge Graph Based Reward Modeling for Diagnostic Reasoning,” has been accepted to the GenAI4Health workshop at NeurIPS 2025! In this paper, we explore a new paradigm for incorporating knowledge graphs (KGs) by framing our problem as reward modeling over KG paths.

We are excited to announce that our paper, “Efficient and Scalable Retrieval in Knowledge Graphs: From Single-Graph to Partitioned Multi-Graph Approaches,” has been accepted to the AMIA 2025 NLP Annual Symposium!

We are excited to announce that our paper “Evaluating Large Language Models for Summarizing Long Clinical Texts and Longitudinal Patient Trajectories” has been accepted to the AMIA 2025 NLP Annual Symposium!

How Do We Know a Clinical Summary is Good? Having spent years working on summarization evaluation in my PhD (before LLM era), I know just how hard it is: There is no single “correct” summary—different summaries may emphasize different relevant points, and human judgment varies.

We’re excited to announce that our paper Anchored Answers: Unravelling Positional Bias in GPT-2’s Multiple-Choice Questions on has been accepted to an upcoming ACL 2025! In this study, we investigate how GPT-2 internally represents and reasons over Multiple-Choice Questions (MCQA) tasks.

General Instructions for Giving a Job Talk at LARK Lab At LARK Lab, we value engaging discussions, deep technical insights, and collaborative learning. A job talk is an opportunity for us to learn about your research, thought process, and how you approach problems.

We are excited to announce the launch of our Reinforcement Learning (RL) Seminar, a biweekly reading and discussion series focused on foundational RL concepts and applications. With several ongoing RL-related projects in our lab and a growing need for structured training among our trainees, we are introducing this seminar-style reading club to foster a deeper understanding of RL principles.

Dr. Yanjun Gao recently spoke at a campus conference on “Engaging with AI”, where she shared insights on the evolving role of AI in research, healthcare, and education. The talk explored how AI innovations, particularly large language models (LLMs), are transforming scientific discovery and decision-making.