Opening Session

Scientific discovery, especially for new drugs and materials, urgently needs our help. The traditional manual approach is highly artisanal, and thus slow and expensive. Most importantly, many commercial drugs or materials have well-documented limitations that have remained unaddressed. In fact, AI for Science has become a rapidly growing field, with NLP researchers playing a central role by organizing shared tasks and competing on various
leaderboards, especially through approaches powered by large language models (LLMs). In this talk, however, I will offer a provocative perspective on why our community may be heading in the wrong direction. Take drug discovery as an example: much of the existing work merely classifies properties of known drugs and thus discovers nothing, or generates
molecules that are chemically impossible to make.<br>
Unlike machines, human scientists are inherently “multilingual,” seamlessly navigating diverse modalities, from natural language in literature to complex scientific data such as molecular structures in knowledge bases. Human scientists also “think before they talk”: they ground their reasoning in deliberate reflection and subject new ideas to critical evaluation and
verification. Against this backdrop, I argue that there is a correctable fundamental mismatch between the way LLMs work and the way scientists traditionally discover and verify new research hypotheses. I propose to design new LLM paradigms by drawing inspiration from the scientific discovery process itself: (1) “Observe”- acquire, represent and integrate
knowledge from multiple data modalities; (2) “Think” - think critically to generate hypotheses; and (3) “Propose and Verify” - verify hypotheses through the Physical World.<br>
As a prototype example, I will present mCLM, a modular Chemical-Language Model that speaks two complementary languages: one that represents molecular building blocks indicative of specific functions and compatible with automated modular assembly, and another that describes these functions in natural language. Experiments on 430 FDA-approved drugs showed that mCLM is capable of significantly improving chemical functions critical to determining drug potentials. mCLM, with only 3B parameters, also achieves improvements in function scores and synthetic accessibility relative to 7 other leading generative AI methods including GPT-5. mCLM can also reason on multiple functions and iteratively self-improve to rescue drug candidates that failed late in clinical trials (“fallen
angels”). Preliminary animal testing results further underscore the promise of this approach.
In the long term, I envision a comprehensive, multi-agent, human-in-the-loop autonomous laboratory, structured around iterative cycles of reasoning, proposal, synthesis, physical testing, feedback, and reasoning to enable never-ending self-improvement and co-evolvement with human scientists.

Keynote 1 - Heng Ji

Interpretability 1

Natural Language Generation 1

NLP Applications 1

Multimodality and Language Grounding to Vision, Robotics and Beyond 1

Computational Social Science 1

Information Extraction and Retrieval 1

Multilinguality and Language Diversity 1

Low-resource Methods for NLP

## Welcome!
"I am excited to welcome you to this year’s edition of the Conference on Empirical Methods in Natural Language Processing! Importantly, it marks the 30th edition of EMNLP. With over 8,000 submissions, more than 3,000 accepted papers, and thousands of attendees, we have come a long way from that first
workshop, which had 14 accepted papers. As the field looks ahead, Suzhou is the fitting location for celebrating this milestone: rooted in a long literary tradition, yet modern and forward-looking, and home to a large share of the NLP community."<br>

*Message from the General Chair, Dirk Hovy*

[**Link to Conference Handbook**](https://drive.google.com/file/d/1johU5QqVVYO4RfH7QcIORr7qrVBdzdwC/view?usp=sharing)





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EMNLP 2025

Celebrate 30 Years of EMNLP! 
EMNLP 2025 will be held in Suzhou, China from November 5th to November 9th, 2025.