12 papers
Uncertainty-Aware Budget Allocation for Adaptive Test-Time Reasoning
Manh Nguyen, Sunil Gupta, Hung Le
Sampling multiple responses improves language model reasoning, but uniform compute allocation is inefficient: easy questions are over-sampled while hard questions remain under-expl…
Decoding the Critique Mechanism in Large Reasoning Models
Hoang Phan, Quang H. Nguyen, Hung T. Q. Le +3
Large Reasoning Models (LRMs) exhibit backtracking and self-verification mechanisms that enable them to revise intermediate steps and reach correct solutions, yielding strong perfo…
Beyond Majority Voting: Efficient Best-Of-N with Radial Consensus Score
Manh Nguyen, Sunil Gupta, Hung Le
Large language models (LLMs) frequently generate multiple candidate responses for a given prompt, yet selecting the most reliable one remains challenging, especially when correctne…
Distance Is All You Need: Radial Dispersion for Uncertainty Estimation in Large Language Models
Manh Nguyen, Sunil Gupta, Hung Le
Detecting uncertainty in large language models (LLMs) is essential for building reliable systems, yet many existing approaches are overly complex and depend on brittle semantic clu…
Retrieval-augmented Decoding for Improving Truthfulness in Open-ended Generation
Manh Nguyen, Sunil Gupta, Hung Le
Ensuring truthfulness in large language models (LLMs) remains a critical challenge for reliable text generation. While supervised fine-tuning and reinforcement learning with human…
Federated Domain Generalization with Latent Space Inversion
Ragja Palakkadavath, Hung Le, Thanh Nguyen-Tang +2
Federated domain generalization (FedDG) addresses distribution shifts among clients in a federated learning framework. FedDG methods aggregate the parameters of locally trained cli…