7 papers
LLM as Forecasting Planner: Training-Free Text Conditioning for Time-Series Foundation Models
Huu Hiep Nguyen, Dung Nguyen, Minh Hoang Nguyen +2
Text-conditioned time-series forecasting predicts a series from both its numerical history and natural-language context, allowing forecasts to account for events and constraints th…
Expert-Guided Forecast Editing for Time-Series Foundation Models
Hung Le, Minh Hoang Nguyen, Manh Nguyen +2
Time-series foundation models can forecast across heterogeneous domains without task-specific training, but their forecasts are fixed once produced and cannot directly incorporate…
Reviving Error Correction in Modern Deep Time-Series Forecasting
Minh Hoang Nguyen, Dai Do, Huu Hiep Nguyen +3
Modern deep-learning models have achieved remarkable success in time-series forecasting. Yet, their performance degrades in long-term prediction due to error accumulation in autore…
SPaCe: Unlocking Sample-Efficient Large Language Models Training With Self-Pace Curriculum Learning
Dai Do, Manh Nguyen, Svetha Venkatesh +1
Large language models (LLMs) have shown strong reasoning capabilities when fine-tuned with reinforcement learning (RL). However, such methods require extensive data and compute, ma…
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…
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…