6 citations · 6 across the 11 of their papers we have counts for
13 papers
Better, Faster, Stronger: Programmatic Skill Learning Best Reduces Agent Cost
Zixi Huang, Xiheng Wang, Andrew Wang +4
Recently, the practice of augmenting LLM agent capability with skills has gained prevalence. We explore the cost effective adaptation of agents to novel domains by means of learnin…
Non-Parametric Machine Text Detection via Multi-View Gaussian Processes
Aleem Khan, Nicholas Andrews
Adversarial conditions such as paraphrasing and targeted style transfer sharply degrade the accuracy of machine text detectors. A document, however, carries multiple complementary…
Unsupervised Style Representation Learning for AI-Text Detection via Paraphrase Inversion
Rafael Rivera Soto, Barry Chen, Nicholas Andrews
The rapid development of large language models (LLMs) has raised concerns about misuse such as plagiarism, misinformation, and automated influence operations, motivating the need f…
Inducing Artificial Uncertainty in Language Models
Sophia Hager, Simon Zeng, Nicholas Andrews
In safety-critical applications, language models should be able to characterize their uncertainty with meaningful probabilities. Many uncertainty quantification approaches require…
Can Coding Agents Reproduce Findings in Computational Materials Science?
Ziyang Huang, Yi Cao, Ali K. Shargh +15
Large language models are increasingly deployed as autonomous coding agents and have achieved remarkably strong performance on software engineering benchmarks. However, it is uncle…
Hell or High Water: Evaluating Agentic Recovery from External Failures
Andrew Wang, Sophia Hager, Adi Asija +2
As language model agents are applied to real world problems of increasing complexity, they will be expected to formulate plans across large search spaces. If those plans fail for r…
Feedback Friction: LLMs Struggle to Fully Incorporate External Feedback
Dongwei Jiang, Alvin Zhang, Andrew Wang +2
Recent studies have shown LLMs possess some ability to improve their responses when given external feedback. However, it remains unclear how effectively and thoroughly these models…
Learning Extrapolative Sequence Transformations from Markov Chains
Sophia Hager, Aleem Khan, Andrew Wang +1
Most successful applications of deep learning involve similar training and test conditions. However, tasks such as biological sequence design involve searching for sequences that i…
Attacks on Machine-Text Detectors Retain Stylistic Fingerprints
Rafael Rivera Soto, Barry Chen, Nicholas Andrews
Despite considerable progress in the development of machine-text detectors, the ease with which machine-text can be manipulated to evade detection has led to suggestions that the p…
Mitigating Paraphrase Attacks on Machine-Text Detectors via Paraphrase Inversion
Rafael Rivera Soto, Barry Chen, Nicholas Andrews
High-quality paraphrases are easy to produce using instruction-tuned language models or specialized paraphrasing models. Although this capability has a variety of benign applicatio…
AnaloBench: Benchmarking the Identification of Abstract and Long-context Analogies
Xiao Ye, Andrew Wang, Jacob Choi +6
Humans regularly engage in analogical thinking, relating personal experiences to current situations (X is analogous to Y because of Z). Analogical thinking allows humans to solve p…
Few-Shot Detection of Machine-Generated Text using Style Representations
Rafael Rivera Soto, Kailin Koch, Aleem Khan +3
The advent of instruction-tuned language models that convincingly mimic human writing poses a significant risk of abuse. However, such abuse may be counteracted with the ability to…
Learning to Generate Text in Arbitrary Writing Styles
Aleem Khan, Andrew Wang, Sophia Hager +1
Prior work in style-controlled text generation has focused on tasks such as emulating the style of prolific literary authors, producing formal or informal text, and mitigating toxi…