9 papers
Beyond Self-Knowledge: Propagating Uncertainty Across Reasoning and Retrieval in LLMs
Chandan Kumar Sah, Xiaoli Lian, Li Zhang
The paper introduces BeyondUncertainty, a method that uses confidence estimates from black‑box language models to decide whether to retrieve external evidence for question answerin…
Beyond "What to Retrieve": Uncertainty in Retrieval-Augmented Code Generation
Chandan Kumar Sah, Xiaoli Lian, Li Zhang
Repository-level code generation relies on heterogeneous evidence whose relevance, compatibility, and completeness are inherently uncertain. Similar-code examples, repository conte…
SACK : Safe Active Continual Koopman Learning for Uncertain Systems with Contractive Guarantees
Chandan Kumar Sah, Rajpal Singh, Jishnu Keshavan
Koopman operator theory provides a powerful framework for representing nonlinear dynamics through a linear operator acting on lifted observables, enabling the use of linear control…
Uncertainty and Fairness Awareness in LLM-Based Recommendation Systems
Chandan Kumar Sah, Xiaoli Lian, Li Zhang +2
Large language models (LLMs) enable powerful zero-shot recommendations by leveraging broad contextual knowledge, yet predictive uncertainty and embedded biases threaten reliability…
PerFairX: Is There a Balance Between Fairness and Personality in Large Language Model Recommendations?
Chandan Kumar Sah
The integration of Large Language Models (LLMs) into recommender systems has enabled zero-shot, personality-based personalization through prompt-based interactions, offering a new…
CleanMAP: Distilling Multimodal LLMs for Confidence-Driven Crowdsourced HD Map Updates
Ankit Kumar Shaw, Kun Jiang, Tuopu Wen +5
The rapid growth of intelligent connected vehicles (ICVs) and integrated vehicle-road-cloud systems has increased the demand for accurate, real-time HD map updates. However, ensuri…