120 citations · 208 across the 25 of their papers we have counts for
5 papers · 2 filters
How Does Response Length Affect Long-Form Factuality
James Xu Zhao, Jimmy Z. J. Liu, Bryan Hooi +1
Large language models (LLMs) are widely used for long-form text generation. However, factual errors in the responses would undermine their reliability. Despite growing attention to…
Beyond 'Aha!': Toward Systematic Meta-Abilities Alignment in Large Reasoning Models
Zhiyuan Hu, Yibo Wang, Hanze Dong +5
Large reasoning models (LRMs) already possess a latent capacity for long chain-of-thought reasoning. Prior work has shown that outcome-based reinforcement learning (RL) can inciden…
Guiding VLM Agents with Process Rewards at Inference Time for GUI Navigation
Zhiyuan Hu, Shiyun Xiong, Yifan Zhang +5
Recent advancements in visual language models (VLMs) have notably enhanced their capabilities in handling complex Graphical User Interface (GUI) interaction tasks. Despite these im…
Balancing Truthfulness and Informativeness with Uncertainty-Aware Instruction Fine-Tuning
Tianyi Wu, Jingwei Ni, Bryan Hooi +5
Instruction fine-tuning (IFT) can increase the informativeness of large language models (LLMs), but may reduce their truthfulness. This trade-off arises because IFT steers LLMs to…
CHiP: Cross-modal Hierarchical Direct Preference Optimization for Multimodal LLMs
Jinlan Fu, Shenzhen Huangfu, Hao Fei +4
Multimodal Large Language Models (MLLMs) still struggle with hallucinations despite their impressive capabilities. Recent studies have attempted to mitigate this by applying Direct…