1 citations · 1 across the 6 of their papers we have counts for
15 papers
Why Self-Rewarding Works: Theoretical Guarantees for Iterative Alignment of Language Models
Shi Fu, Yingjie Wang, Shengchao Hu +2
Self-Rewarding Language Models (SRLMs) achieve notable success in iteratively improving alignment without external feedback. Yet, despite their striking empirical progress, the cor…
EchoBench: Benchmarking Sycophancy in Medical Large Vision-Language Models
Botai Yuan, Yutian Zhou, Yingjie Wang +9
Recent benchmarks for medical Large Vision-Language Models (LVLMs) emphasize leaderboard accuracy, overlooking reliability and safety. We study sycophancy -- models' tendency to un…
CoVeR: Conformal Calibration for Versatile and Reliable Autoregressive Next-Token Prediction
Yuzhu Chen, Yingjie Wang, Shunyu Liu +2
Autoregressive pre-trained models combined with decoding methods have achieved impressive performance on complex reasoning tasks. While mainstream decoding strategies such as beam…
Consistent Paths Lead to Truth: Self-Rewarding Reinforcement Learning for LLM Reasoning
Kongcheng Zhang, Qi Yao, Shunyu Liu +5
Recent advances of Reinforcement Learning (RL) have highlighted its potential in complex reasoning tasks, yet effective training often relies on external supervision, which limits…
Multimodal Reasoning Agent for Zero-Shot Composed Image Retrieval
Rong-Cheng Tu, Wenhao Sun, Hanzhe You +4
Zero-Shot Composed Image Retrieval (ZS-CIR) aims to retrieve target images given a compositional query, consisting of a reference image and a modifying text-without relying on anno…
MLLM-Guided VLM Fine-Tuning with Joint Inference for Zero-Shot Composed Image Retrieval
Rong-Cheng Tu, Zhao Jin, Jingyi Liao +4
Existing Zero-Shot Composed Image Retrieval (ZS-CIR) methods typically train adapters that convert reference images into pseudo-text tokens, which are concatenated with the modifyi…