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 because easy questions are over-sampled while hard questions remain und…
Efficient Best-Of-N with Radial Consensus Score
Manh Nguyen, Sunil Gupta, Hung Le
Solving complex problems with large language models (LLMs) often requires selecting the best answer from multiple candidate responses (best-of-), yet identifying the most reliab…
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…
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…
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…
Uncertainty-Guided Checkpoint Selection for Reinforcement Finetuning of Large Language Models
Manh Nguyen, Dung Nguyen, Dai Do +2
Reinforcement learning (RL) finetuning is crucial to aligning large language models (LLMs), but the process is notoriously unstable and exhibits high variance across model checkpoi…
Probabilities Are All You Need: A Probability-Only Approach to Uncertainty Estimation in Large Language Models
Manh Nguyen, Sunil Gupta, Hung Le
Large Language Models (LLMs) exhibit strong performance across various natural language processing (NLP) tasks but remain vulnerable to hallucinations, generating factually incorre…
GRAD: Graph-Retrieved Adaptive Decoding for Hallucination Mitigation
Manh Nguyen, Sunil Gupta, Dai Do +1
Hallucination mitigation remains a persistent challenge for large language models (LLMs), even as model scales grow. Existing approaches often rely on external knowledge sources, s…
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…
DmC: Nearest Neighbor Guidance Diffusion Model for Offline Cross-domain Reinforcement Learning
Linh Le Pham Van, Minh Hoang Nguyen, Duc Kieu +3
Cross-domain offline reinforcement learning (RL) seeks to enhance sample efficiency in offline RL by utilizing additional offline source datasets. A key challenge is to identify an…
Hybrid Cross-domain Robust Reinforcement Learning
Linh Le Pham Van, Minh Hoang Nguyen, Hung Le +2
Robust reinforcement learning (RL) aims to learn policies that remain effective despite uncertainties in its environment, which frequently arise in real-world applications due to v…
Beyond the Known: Decision Making with Counterfactual Reasoning Decision Transformer
Minh Hoang Nguyen, Linh Le Pham Van, Thommen George Karimpanal +2
Decision Transformers (DT) play a crucial role in modern reinforcement learning, leveraging offline datasets to achieve impressive results across various domains. However, DT requi…