11 citations · 32 across the 41 of their papers we have counts for
9 papers · 2 filters
WithdrarXiv: A Large-Scale Dataset for Retraction Study
Delip Rao, Jonathan Young, Thomas Dietterich +1
Retractions play a vital role in maintaining scientific integrity, yet systematic studies of retractions in computer science and other STEM fields remain scarce. We present Withdra…
StyleDistance: Stronger Content-Independent Style Embeddings with Synthetic Parallel Examples
Ajay Patel, Jiacheng Zhu, Justin Qiu +4
Style representations aim to embed texts with similar writing styles closely and texts with different styles far apart, regardless of content. However, the contrastive triplets oft…
Multilingual Retrieval Augmented Generation for Culturally-Sensitive Tasks: A Benchmark for Cross-lingual Robustness
Bryan Li, Fiona Luo, Samar Haider +7
The paradigm of retrieval-augmented generated (RAG) helps mitigate hallucinations of large language models (LLMs). However, RAG also introduces biases contained within the retrieve…
Uncovering Differences in Persuasive Language in Russian versus English Wikipedia
Bryan Li, Aleksey Panasyuk, Chris Callison-Burch
We study how differences in persuasive language across Wikipedia articles, written in either English and Russian, can uncover each culture's distinct perspective on different subje…
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…
Rel-A.I.: An Interaction-Centered Approach To Measuring Human-LM Reliance
Kaitlyn Zhou, Jena D. Hwang, Xiang Ren +3
The ability to communicate uncertainty, risk, and limitation is crucial for the safety of large language models. However, current evaluations of these abilities rely on simple cali…
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…
TinyStyler: Efficient Few-Shot Text Style Transfer with Authorship Embeddings
Zachary Horvitz, Ajay Patel, Kanishk Singh +3
The goal of text style transfer is to transform the style of texts while preserving their original meaning, often with only a few examples of the target style. Existing style trans…