7 papers
Cooperative Profiles Predict Multi-Agent LLM Team Performance in AI for Science Workflows
Shivani Kumar, Adarsh Bharathwaj, David Jurgens
Multi-agent systems built from teams of large language models (LLMs) are increasingly deployed for collaborative scientific reasoning and problem-solving. These systems require age…
Think Multilingual, Not Harder: A Data-Efficient Framework for Teaching Reasoning Models to Code-Switch
Eleanor M. Lin, David Jurgens
Recent developments in reasoning capabilities have enabled large language models to solve increasingly complex mathematical, symbolic, and logical tasks. Interestingly, while reaso…
Unstructured Evidence Attribution for Long Context Query Focused Summarization
Dustin Wright, Zain Muhammad Mujahid, Lu Wang +2
Large language models (LLMs) are capable of generating coherent summaries from very long contexts given a user query, and extracting and citing evidence spans helps improve the tru…
One Model, Many Morals: Uncovering Cross-Linguistic Misalignments in Computational Moral Reasoning
Sualeha Farid, Jayden Lin, Zean Chen +2
Large Language Models (LLMs) are increasingly deployed in multilingual and multicultural environments where moral reasoning is essential for generating ethically appropriate respon…
Structured Moral Reasoning in Language Models: A Value-Grounded Evaluation Framework
Mohna Chakraborty, Lu Wang, David Jurgens
Large language models (LLMs) are increasingly deployed in domains requiring moral understanding, yet their reasoning often remains shallow, and misaligned with human reasoning. Unl…
Evaluation Framework for AI Systems in "the Wild"
Sarah Jabbour, Trenton Chang, Anindya Das Antar +13
Generative AI (GenAI) models have become vital across industries, yet current evaluation methods have not adapted to their widespread use. Traditional evaluations often rely on ben…