47 citations · 106 across the 29 of their papers we have counts for
12 papers · 1 filter
InfoSFT: Learn More and Forget Less with Information-Aware Token Weighting
Mahdi Sabbaghi, George Pappas, Adel Javanmard +1
Supervised fine-tuning (SFT) provides the standard approach for teaching LLMs new behaviors from offline expert demonstrations. However, standard SFT uniformly fits all samples --…
Multi-Round Human-AI Collaboration with User-Specified Requirements
Sima Noorani, Shayan Kiyani, Hamed Hassani +1
As humans increasingly rely on multiround conversational AI for high stakes decisions, principled frameworks are needed to ensure such interactions reliably improve decision qualit…
When to Trust the Cheap Check: Weak and Strong Verification for Reasoning
Shayan Kiyani, Sima Noorani, George Pappas +1
Reasoning with LLMs increasingly unfolds inside a broader verification loop. Internally, systems use cheap checks, such as self-consistency or proxy rewards, which we call weak ver…
Robust Policy Optimization to Prevent Catastrophic Forgetting
Mahdi Sabbaghi, George Pappas, Adel Javanmard +1
Large language models are commonly trained through multi-stage post-training: first via RLHF, then fine-tuned for other downstream objectives. Yet even small downstream updates can…
Conformal Prediction Beyond the Seen: A Missing Mass Perspective for Uncertainty Quantification in Generative Models
Sima Noorani, Shayan Kiyani, George Pappas +1
Uncertainty quantification (UQ) is essential for safe deployment of generative AI models such as large language models (LLMs), especially in high stakes applications. Conformal pre…
Decision Theoretic Foundations for Conformal Prediction: Optimal Uncertainty Quantification for Risk-Averse Agents
Shayan Kiyani, George Pappas, Aaron Roth +1
A fundamental question in data-driven decision making is how to quantify the uncertainty of predictions in ways that can usefully inform downstream action. This interface between p…