15 citations · 20 across the 8 of their papers we have counts for
9 papers
Pluralistic Off-policy Evaluation and Alignment
Chengkai Huang, Junda Wu, Zhouhang Xie +6
Personalized preference alignment for LLMs with diverse human preferences requires evaluation and alignment methods that capture pluralism. Most existing preference alignment datas…
RCStat: A Statistical Framework for using Relative Contextualization in Transformers
Debabrata Mahapatra, Shubham Agarwal, Apoorv Saxena +1
Identifying which tokens and activations truly influence model predictions is critical for both the efficiency and interpretability of large auto-regressive language models. Yet in…
CachePrune: Teaching LLMs What Not to Follow via KV-Cache Editing
Rui Wang, Junda Wu, Yu Xia +6
Large Language Models (LLMs) are susceptible to indirect prompt injection attacks, where the model inadvertently responds to instructions injected into the prompt context. This vul…
Perception-aware Sampling for Scatterplot Visualizations
Zafeiria Moumoulidou, Hamza Elhamdadi, Ke Yang +3
Visualizing data is often a crucial first step in data analytics workflows, but growing data sizes pose challenges due to computational and visual perception limitations. As a resu…
Cache-Craft: Managing Chunk-Caches for Efficient Retrieval-Augmented Generation
Shubham Agarwal, Sai Sundaresan, Subrata Mitra +6
Retrieval-Augmented Generation (RAG) is often used with Large Language Models (LLMs) to infuse domain knowledge or user-specific information. In RAG, given a user query, a retrieve…
Personalized Multimodal Large Language Models: A Survey
Junda Wu, Hanjia Lyu, Yu Xia +24
Multimodal Large Language Models (MLLMs) have become increasingly important due to their state-of-the-art performance and ability to integrate multiple data modalities, such as tex…
Personalization of Large Language Models: A Survey
Zhehao Zhang, Ryan A. Rossi, Branislav Kveton +18
Personalization of Large Language Models (LLMs) has recently become increasingly important with a wide range of applications. Despite the importance and recent progress, most exist…
Visual Prompting in Multimodal Large Language Models: A Survey
Junda Wu, Zhehao Zhang, Yu Xia +12
Multimodal large language models (MLLMs) equip pre-trained large-language models (LLMs) with visual capabilities. While textual prompting in LLMs has been widely studied, visual pr…
A Framework for Fine-Tuning LLMs using Heterogeneous Feedback
Ryan Aponte, Ryan A. Rossi, Shunan Guo +5
Large language models (LLMs) have been applied to a wide range of tasks, including text summarization, web navigation, and chatbots. They have benefitted from supervised fine-tunin…