10 papers
Beyond Static Dialogues: Benchmarking Realistic, Heterogeneous, and Evolving Long-Term Memory
Han Zhang, Zihao Tang, Xin Yu +8
In existing memory benchmarks for Large Language Models (LLMs), the evaluated dialogue sessions often lack long-term semantic consistency, and the underlying personas tend to be fl…
Dr. DocBench: A Comprehensive Benchmark for Expert-Level and Difficult Document Parsing
Minglai Yang, Xinyan Velocity Yu, Pengyuan Li +22
Document parsing and recognition are fundamental capabilities for vision-language models (VLMs) and document processing systems. However, existing Optical Character Recognition (OC…
Don't Let It Hallucinate: Premise Verification via Retrieval-Augmented Logical Reasoning
Yuehan Qin, Shawn Li, Yi Nian +3
Large language models (LLMs) have shown substantial capacity for generating fluent, contextually appropriate responses. However, they can produce hallucinated outputs, especially w…
EXACT: Explicit Attribute-Guided Decoding-Time Personalization
Xin Yu, Hanwen Xing, Lingzhou Xue
Achieving personalized alignment requires adapting large language models to each user's evolving context. While decoding-time personalization offers a scalable alternative to train…
Cancer-Myth: Evaluating Large Language Models on Patient Questions with False Presuppositions
Wang Bill Zhu, Tianqi Chen, Xinyan Velocity Yu +6
Cancer patients are increasingly turning to large language models (LLMs) for medical information, making it critical to assess how well these models handle complex, personalized qu…
Towards Better Optimization For Listwise Preference in Diffusion Models
Jiamu Bai, Xin Yu, Meilong Xu +6
Reinforcement learning from human feedback (RLHF) has proven effectiveness for aligning text-to-image (T2I) diffusion models with human preferences. Although Direct Preference Opti…