8 papers · 1 filter
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…
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…
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…
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…
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…
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…