3 citations · 3 across the 6 of their papers we have counts for
7 papers
Inference Auctions
Keegan Harris, Siddharth Prasad, Asher Trockman +2
When inference demand exceeds available compute capacity, model providers must decide which requests should be served first. Users have different tolerances for delay from an LLM A…
Post-Training at the Edge of Detectability: A Game-Theoretic Approach to Fine-Tuning
Keegan Harris, Brian W. Lee, Ian Waudby-Smith +3
Reinforcement learning (RL) fine-tuning is widely used in language model training to improve performance on a target task while limiting drift from a reference policy. A standard w…
In-Context Credit Assignment via the Core
Keegan Harris, Siddharth Prasad, Asher Trockman
We propose incentive-aligned mechanisms for in-context credit assignment: the task of assigning credit for AI-generated content among creators whose intellectual property appears i…
Can ChatGPT Learn My Life From a Week of First-Person Video?
Keegan Harris
Motivated by recent improvements in generative AI and wearable camera devices (e.g. smart glasses and AI-enabled pins), I investigate the ability of foundation models to learn abou…
Should You Use Your Large Language Model to Explore or Exploit?
Keegan Harris, Aleksandrs Slivkins
We evaluate the ability of the current generation of large language models (LLMs) to help a decision-making agent facing an exploration-exploitation tradeoff. While previous work h…
Can large language models explore in-context?
Akshay Krishnamurthy, Keegan Harris, Dylan J. Foster +2
We investigate the extent to which contemporary Large Language Models (LLMs) can engage in exploration, a core capability in reinforcement learning and decision making. We focus on…
Algorithmic Persuasion Through Simulation
Keegan Harris, Nicole Immorlica, Brendan Lucier +1
We study a Bayesian persuasion game where a sender wants to persuade a receiver to take a binary action, such as purchasing a product. The sender is informed about the (real-valued…