Peilin Cai

Li Li, Peilin Cai, Ryan A. Rossi et al.

We present PersonaConvBench, a large-scale benchmark for evaluating personalized reasoning and generation in multi-turn conversations with large language models (LLMs). Unlike existing work that focuses on either personalization or conversational structure in isolation, PersonaConvBench integrates both, offering three core tasks: sentence classification, impact regression, and user-centric text generation across ten diverse Reddit-based domains. This design enables systematic analysis of how personalized conversational context shapes LLM outputs in realistic multi-user scenarios. We benchmark several commercial and open-source LLMs under a unified prompting setup and observe that incorporating personalized history yields substantial performance improvements, including a 198 percent relative gain over the best non-conversational baseline in sentiment classification. By releasing PersonaConvBench with evaluations and code, we aim to support research on LLMs that adapt to individual styles, track long-term context, and produce contextually rich, engaging responses.

Peilin Cai

This paper investigates the application of advanced image segmentation techniques to analyze C-fos immediate early gene expression, a crucial marker for neural activity. Due to the complexity and high variability of neural circuits, accurate segmentation of C-fos images is paramount for the development of new insights into neural function. Amidst this backdrop, this research aims to improve accuracy and minimize manual intervention in C-fos image segmentation by leveraging the capabilities of CNNs and the Unet model. We describe the development of a novel workflow for the segmentation process involving Convolutional Neural Networks (CNNs) and the Unet model, demonstrating their efficiency in various image segmentation tasks. Our workflow incorporates pre-processing steps such as cropping, image feature extraction, and clustering for the training dataset selection. We used an AutoEncoder model to extract features and implement constrained clustering to identify similarities and differences in image types. Additionally, we utilized manual and automatic labeling approaches to enhance the performance of our model. We demonstrated the effectiveness of our method in distinguishing areas with significant C-fos expression from normal tissue areas. Lastly, we implemented a modified Unet network for the detection of C-fos expressions. This research contributes to the development of more efficient and automated image segmentation methods, advancing the understanding of neural function in neuroscience research.