18 citations · 75 across the 21 of their papers we have counts for
6 papers · 1 filter
High-Value Training Data from Noisy, Diverse Sources
Hedi Driss, Evan Frick, Anastasios N. Angelopoulos +4
Training state-of-the-art models increasingly requires learning from large, heterogeneous datasets whose examples vary in quality, domain relevance, and source of supervision. Data…
Corrupt Plans, Clean Traces: Evading Chain-of-Thought Monitoring with Plan Injection
Keertana Chidambaram, Andrew Ilyas, Vasilis Syrgkanis
Chain-of-thought (CoT) monitoring is a safety strategy where the reasoning of a large language model "actor" is inspected by a "monitor" (often another language model) for signs of…
Data Attribution via Sketched Metadifferentiation
Yuxi Chen, Hamza Golubovic, Han Tong +2
Data attribution seeks to quantify how individual training examples shape a model's predictions and underpins problems including data valuation, machine unlearning, and model inter…
Pick Your Poison: Learning to Select Poison Sets for Stronger LLM Backdoor Attacks
Aashiq Muhamed, Mona T. Diab, Virginia Smith +2
Backdoor poisoning attacks add poisoned examples to otherwise-clean finetuning data, pairing a trigger with a target behavior that the model learns to produce when the trigger appe…
Using AI Agents to Automate Black-Box Audits of Personalization Algorithms at Scale
Alessandro Morosini, Sarah H. Cen, Andrew Ilyas +3
Personalization algorithms determine what content users encounter on online platforms. Auditing these systems is difficult because independent auditors have only black-box access t…
Justified or Just Convincing? Error Verifiability as a Dimension of LLM Quality
Xiaoyuan Zhu, Kimberly Le Truong, Riccardo Fogliato +8
As LLMs are deployed in high-stakes settings, users must judge the correctness of individual responses, often relying on model-generated justifications such as reasoning chains or…