7 papers
InA-Probe: Instruction-Aware Active Probing for Time Series Forecasting with LLMs
Peiliang Gong, Emadeldeen Eldele, Chenyu Liu +8
Large Language Models (LLMs) have recently demonstrated impressive potential for time series forecasting. However, existing methods predominantly rely on passive modality alignment…
BoundRL: Efficient Structured Text Segmentation through Reinforced Boundary Generation
Haoyuan Li, Zhengyuan Shen, Sullam Jeoung +6
Structured texts refer to texts containing structured elements beyond plain texts, such as code snippets and placeholders. Such structured texts increasingly require segmentation i…
Detecting Contextual Hallucinations in LLMs with Frequency-Aware Attention
Siya Qi, Yudong Chen, Runcong Zhao +6
Hallucination detection is critical for ensuring the reliability of large language models (LLMs) in context-based generation. Prior work has explored intrinsic signals available du…
Train Less, Learn More: Adaptive Efficient Rollout Optimization for Group-Based Reinforcement Learning
Zhi Zhang, Zhen Han, Costas Mavromatis +9
Reinforcement learning (RL) plays a central role in large language model (LLM) post-training. Among existing approaches, Group Relative Policy Optimization (GRPO) is widely used, e…
VERA: Identifying and Leveraging Visual Evidence Retrieval Heads in Long-Context Understanding
Rongcan Pei, Huan Li, Fang Guo +1
While Vision-Language Models (VLMs) have shown promise in textual understanding, they face significant challenges when handling long context and complex reasoning tasks. In this pa…
SQL-Trail: Multi-Turn Reinforcement Learning with Interleaved Feedback for Text-to-SQL
Harper Hua, Zhen Han, Zhengyuan Shen +9
While large language models (LLMs) have substantially improved Text-to-SQL generation, a pronounced gap remains between AI systems and human experts on challenging benchmarks such…