16 citations · 25 across the 13 of their papers we have counts for
13 papers
One Frozen Simulator Is Not Enough: Simulator Collapse in Multi-Agent RL
Simon Yu, Nicholas Tomlin, Marwa Abdulhai +7
Multi-agent reinforcement learning for human-AI interaction typically relies on a single large language model to simulate user behavior. We show that this approach systematically f…
Discovering High-Quality Chess Puzzles with Offline Reinforcement Learning
Allen Nie, Anirudhan Badrinath, Nicholas Tomlin +5
Learning and skill mastery require extensive and deliberate practice. In many learning settings, producing high-quality pedagogical materials can require a high level of domain exp…
Calibrate-Then-Act: Cost-Aware Exploration in LLM Agents
Wenxuan Ding, Nicholas Tomlin, Greg Durrett
LLM agents are deployed in environments where they must interact to acquire information. In these scenarios, the agent must reason about inherent cost-uncertainty tradeoffs in how…
Measuring General Intelligence with Generated Games
Vivek Verma, David Huang, William Chen +2
We present gg-bench, a collection of game environments designed to evaluate general reasoning capabilities in language models. Unlike most static benchmarks, gg-bench is a data gen…
Efficacy of Language Model Self-Play in Non-Zero-Sum Games
Austen Liao, Nicholas Tomlin, Dan Klein
Game-playing agents like AlphaGo have achieved superhuman performance through self-play, which is theoretically guaranteed to yield optimal policies in competitive games. However,…
Autonomous Evaluation and Refinement of Digital Agents
Jiayi Pan, Yichi Zhang, Nicholas Tomlin +3
We show that domain-general automatic evaluators can significantly improve the performance of agents for web navigation and device control. We experiment with multiple evaluation m…
Decision-Oriented Dialogue for Human-AI Collaboration
Jessy Lin, Nicholas Tomlin, Jacob Andreas +1
We describe a class of tasks called decision-oriented dialogues, in which AI assistants such as large language models (LMs) must collaborate with one or more humans via natural lan…
Ghostbuster: Detecting Text Ghostwritten by Large Language Models
Vivek Verma, Eve Fleisig, Nicholas Tomlin +1
We introduce Ghostbuster, a state-of-the-art system for detecting AI-generated text. Our method works by passing documents through a series of weaker language models, running a str…
Revisiting Entropy Rate Constancy in Text
Vivek Verma, Nicholas Tomlin, Dan Klein
The uniform information density (UID) hypothesis states that humans tend to distribute information roughly evenly across an utterance or discourse. Early evidence in support of the…
Neural Unsupervised Reconstruction of Protolanguage Word Forms
Andre He, Nicholas Tomlin, Dan Klein
We present a state-of-the-art neural approach to the unsupervised reconstruction of ancient word forms. Previous work in this domain used expectation-maximization to predict simple…
Pragmatics in Language Grounding: Phenomena, Tasks, and Modeling Approaches
Daniel Fried, Nicholas Tomlin, Jennifer Hu +2
People rely heavily on context to enrich meaning beyond what is literally said, enabling concise but effective communication. To interact successfully and naturally with people, us…
Automated Crossword Solving
Eric Wallace, Nicholas Tomlin, Albert Xu +4
We present the Berkeley Crossword Solver, a state-of-the-art approach for automatically solving crossword puzzles. Our system works by generating answer candidates for each crosswo…
Understanding Game-Playing Agents with Natural Language Annotations
Nicholas Tomlin, Andre He, Dan Klein
We present a new dataset containing 10K human-annotated games of Go and show how these natural language annotations can be used as a tool for model interpretability. Given a board…