1 citations · 3 across the 11 of their papers we have counts for
5 papers · 1 filter
FIGS: Evaluating Multi-Turn Sycophancy Without Penalizing Empathy
Sidharth Pulipaka, Ruta Binkyte, Ivaxi Sheth +1
Large language models frequently fail to balance staying truthful with being supportive. They often exhibit sycophancy in responses to users, agreeing with false claims, offering u…
Norms at a Price: Why RL-Based Alignment Can Promise Conditional Compliance at Best
Kevin Baum, Rūta Binkytė, Felix Jahn
AI agents sometimes act aligned when they infer they are being tested, and differently when not. We argue this is not an anomaly but what current training regimes are structured to…
Last Translation Benchmark
Vilém Zouhar, Niyati Bafna, Mukund Choudhary +263
For scientific progress, we need benchmarks that test the limits of state-of-the-art models, and evaluation methods that inform us about failure cases. As models get stronger, stan…
Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solution
Ruta Binkyte, Ivaxi Sheth, Zhijing Jin +3
As artificial intelligence (AI), including machine learning (ML) models and foundation models (FMs), are increasingly deployed in high-stakes domains, ensuring their trustworthines…
Inspectable AI for Science: A Research Object Approach to Generative AI Governance
Ruta Binkyte, Sharif Abuaddba, Chamikara Mahawaga +3
This paper introduces AI as a Research Object (AI-RO), a paradigm for governing the use of generative AI in scientific research. Instead of debating whether AI is an author or mere…