1 citations · 3 across the 11 of their papers we have counts for
7 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…
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…
Interactional Fairness in LLM Multi-Agent Systems: An Evaluation Framework
Ruta Binkyte
As large language models (LLMs) are increasingly used in multi-agent systems, questions of fairness should extend beyond resource distribution and procedural design to include the…
Safety Must Precede the Deployment of Open-Ended AI
Ivaxi Sheth, Jan Wehner, Sahar Abdelnabi +2
AI advancements have been significantly driven by a combination of foundation models and curiosity-driven learning aimed at increasing capability and adaptability. Within this land…
LLM4GRN: Discovering Causal Gene Regulatory Networks with LLMs -- Evaluation through Synthetic Data Generation
Tejumade Afonja, Ivaxi Sheth, Ruta Binkyte +4
Gene regulatory networks (GRNs) represent the causal relationships between transcription factors (TFs) and target genes in single-cell RNA sequencing (scRNA-seq) data. Understandin…