24 citations · 50 across the 14 of their papers we have counts for
10 papers · 1 filter
Overhearing LLM Agents: A Survey, Taxonomy, and Roadmap
Andrew Zhu, Chris Callison-Burch
Imagine AI assistants that enhance conversations without interrupting them: quietly providing relevant information during a medical consultation, seamlessly preparing materials as…
First Steps Towards Overhearing LLM Agents: A Case Study With Dungeons & Dragons Gameplay
Andrew Zhu, Evan Osgood, Chris Callison-Burch
Much work has been done on conversational LLM agents which directly assist human users with tasks. We present an alternative paradigm for interacting with LLM agents, which we call…
GenAI Content Detection Task 3: Cross-Domain Machine-Generated Text Detection Challenge
Liam Dugan, Andrew Zhu, Firoj Alam +3
Recently there have been many shared tasks targeting the detection of generated text from Large Language Models (LLMs). However, these shared tasks tend to focus either on cases wh…
You Have Thirteen Hours in Which to Solve the Labyrinth: Enhancing AI Game Masters with Function Calling
Jaewoo Song, Andrew Zhu, Chris Callison-Burch
Developing a consistent and reliable AI game master for text-based games is a challenging task due to the limitations of large language models (LLMs) and the complexity of the game…
ReDel: A Toolkit for LLM-Powered Recursive Multi-Agent Systems
Andrew Zhu, Liam Dugan, Chris Callison-Burch
Recently, there has been increasing interest in using Large Language Models (LLMs) to construct complex multi-agent systems to perform tasks such as compiling literature reviews, d…
RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectors
Liam Dugan, Alyssa Hwang, Filip Trhlik +5
Many commercial and open-source models claim to detect machine-generated text with extremely high accuracy (99% or more). However, very few of these detectors are evaluated on shar…
FanOutQA: A Multi-Hop, Multi-Document Question Answering Benchmark for Large Language Models
Andrew Zhu, Alyssa Hwang, Liam Dugan +1
One type of question that is commonly found in day-to-day scenarios is ``fan-out'' questions, complex multi-hop, multi-document reasoning questions that require finding information…
CALYPSO: LLMs as Dungeon Masters' Assistants
Andrew Zhu, Lara J. Martin, Andrew Head +1
The role of a Dungeon Master, or DM, in the game Dungeons & Dragons is to perform multiple tasks simultaneously. The DM must digest information about the game setting and monsters,…
FIREBALL: A Dataset of Dungeons and Dragons Actual-Play with Structured Game State Information
Andrew Zhu, Karmanya Aggarwal, Alexander Feng +2
Dungeons & Dragons (D&D) is a tabletop roleplaying game with complex natural language interactions between players and hidden state information. Recent work has shown that large la…
I Cast Detect Thoughts: Learning to Converse and Guide with Intents and Theory-of-Mind in Dungeons and Dragons
Pei Zhou, Andrew Zhu, Jennifer Hu +5
We propose a novel task, G4C, to study teacher-student natural language interactions in a goal-driven and grounded environment. Dungeons and Dragons (D&D), a role-playing game, pro…