Talks

• EMNLP2025 Keynote: Heng Ji
• EMNLP2025 Eo: Expert Generalization in MOE
• EMNLP2025 Wu: Zero Shot Graph Learning
• EMNLP2025: MUSE, MCTS Driven Red Teaming

Posters

• EMNLP2025 Wednesday Morning Posters
• EMNLP2025 Friday Afternoon Posters
• EMNLP2025 Extra Things

Takes

• although parsing maybe dead for natural language, structure helps parse scientific information (i.e. drugs, molecules, proteins, etc.)
• two idea: 1) how to formalize approach mathematically 2) what can LMs do that humans can’t do?
• information-rich statefulness + constraints for pruning space is the unlock for ability to build on previous results; i.e. “critical thinking”

Tasks to Do

• EMNLP2025 Fan: medium is not the message: I wonder if we can remove keyboard based signals from BM25 using this method
• EMNLP2025 Xu: tree of prompting: a bunch of multi-hop retrieval datasets to benchmark for RAG-DOLL
• EMNLP2025 Bai: understanding and leveraging expert specialization of context faithfulness: a good set of retrieval benchmarks

Tasks Can Do

• EMNLP2025 Keynote: Heng Ji: “protein LLM requires early exit to capture dynamical Beauvoir”; what if we Mixture of Depth a protein LM?
• EMNLP2025 Hutson: measuring informative of open and questions: formalize this as a rho– POMDP , or use actual value of information measures with Belman backup
• EMNLP2025 Karamanolakis: interactive machine teaching: use MCTS UCB to pick the next set of constitutions to optimize for
• EMNLP2025 Yu: Long-Context LM Fail in Basic Retrieval: I wonder how thoughtbubbles do on the dataset
• EMNLP2025 Bai: understanding and leveraging expert specialization of context faithfulness: could be interesting using the same freeze/clamping technique for cultural work
• EMNLP2025 Vasu: literature grounded hypothesis generation: maybe could use its same hypothesis generation pipeline for RAG
• EMNLP2025Li: enhancing RAG RESPONSE evaluator: maybe could be useful to use to evaluate edge rewards for RAGDOLL