124 citations · 237 across the 33 of their papers we have counts for
22 papers · 1 filter
Learning to Orchestrate Agents under Uncertainty
Mary Chriselda Antony Oliver, Lan Jiang, Aaron Bundi Anampiu +3
Adaptive orchestration of heterogeneous agents requires making sequential delegation decisions under uncertain and evolving agent behaviour, e.g., coordinating specialised AI model…
Faster, Cheaper, Better: Multi-Objective Hyperparameter Optimization for LLM and RAG Systems
Matthew Barker, Andrew Bell, Evan Thomas +3
While Retrieval Augmented Generation (RAG) has emerged as a popular technique for improving Large Language Model (LLM) systems, it introduces a large number of choices, parameters…
Large Language Models Must Be Taught to Know What They Don't Know
Sanyam Kapoor, Nate Gruver, Manley Roberts +7
When using large language models (LLMs) in high-stakes applications, we need to know when we can trust their predictions. Some works argue that prompting high-performance LLMs is s…
Representational Alignment Supports Effective Machine Teaching
Ilia Sucholutsky, Katherine M. Collins, Maya Malaviya +11
A good teacher should not only be knowledgeable, but should also be able to communicate in a way that the student understands -- to share the student's representation of the world.…
Evaluating Language Models for Mathematics through Interactions
Katherine M. Collins, Albert Q. Jiang, Simon Frieder +11
There is much excitement about the opportunity to harness the power of large language models (LLMs) when building problem-solving assistants. However, the standard methodology of e…
Learning Personalized Decision Support Policies
Umang Bhatt, Valerie Chen, Katherine M. Collins +4
Individual human decision-makers may benefit from different forms of support to improve decision outcomes, but when each form of support will yield better outcomes? In this work, w…