activity
20192026
most citedLLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understanding

27 citations · 153 across the 63 of their papers we have counts for

collaborators
Showing 2024Show all

15 papers · 1 filter

cs.CV2024

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…

cs.CL2024★ 2 cited

Multi-LLM Text Summarization

Jiangnan Fang, Cheng-Tse Liu, Jieun Kim +9

In this work, we propose a Multi-LLM summarization framework, and investigate two different multi-LLM strategies including centralized and decentralized. Our multi-LLM summarizatio…

cs.HC2024

Optimizing Data Delivery: Insights from User Preferences on Visuals, Tables, and Text

Reuben Luera, Ryan Rossi, Franck Dernoncourt +9

In this work, we research user preferences to see a chart, table, or text given a question asked by the user. This enables us to understand when it is best to show a chart, table,…

cs.CL2024★ 2 cited

DynaSaur: Large Language Agents Beyond Predefined Actions

Dang Nguyen, Viet Dac Lai, Seunghyun Yoon +9

Existing LLM agent systems typically select actions from a fixed and predefined set at every step. While this approach is effective in closed, narrowly scoped environments, it pres…

cs.CL2024★ 15 cited

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…

cs.HC2024★ 2 cited

Survey of User Interface Design and Interaction Techniques in Generative AI Applications

Reuben Luera, Ryan A. Rossi, Alexa Siu +10

The applications of generative AI have become extremely impressive, and the interplay between users and AI is even more so. Current human-AI interaction literature has taken a broa…

cs.CL2024★ 6 cited

A Survey of Small Language Models

Chien Van Nguyen, Xuan Shen, Ryan Aponte +25

Small Language Models (SLMs) have become increasingly important due to their efficiency and performance to perform various language tasks with minimal computational resources, maki…

cs.CL2024

VipAct: Visual-Perception Enhancement via Specialized VLM Agent Collaboration and Tool-use

Zhehao Zhang, Ryan Rossi, Tong Yu +7

While vision-language models (VLMs) have demonstrated remarkable performance across various tasks combining textual and visual information, they continue to struggle with fine-grai…

cs.CL2024

A Multi-LLM Debiasing Framework

Deonna M. Owens, Ryan A. Rossi, Sungchul Kim +7

Large Language Models (LLMs) are powerful tools with the potential to benefit society immensely, yet, they have demonstrated biases that perpetuate societal inequalities. Despite s…

cs.CL2024

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…

cs.CL2024

KaPQA: Knowledge-Augmented Product Question-Answering

Swetha Eppalapally, Daksh Dangi, Chaithra Bhat +8

Question-answering for domain-specific applications has recently attracted much interest due to the latest advancements in large language models (LLMs). However, accurately assessi…

cs.CL2024★ 2 cited

LongLaMP: A Benchmark for Personalized Long-form Text Generation

Ishita Kumar, Snigdha Viswanathan, Sushrita Yerra +11

Long-text generation is seemingly ubiquitous in real-world applications of large language models such as generating an email or writing a review. Despite the fundamental importance…

cs.IR2024

Augmenting Textual Generation via Topology Aware Retrieval

Yu Wang, Nedim Lipka, Ruiyi Zhang +6

Despite the impressive advancements of Large Language Models (LLMs) in generating text, they are often limited by the knowledge contained in the input and prone to producing inaccu…

cs.HC2024

Supporting Business Document Workflows via Collection-Centric Information Foraging with Large Language Models

Raymond Fok, Nedim Lipka, Tong Sun +1

Knowledge workers often need to extract and analyze information from a collection of documents to solve complex information tasks in the workplace, e.g., hiring managers reviewing…

cs.CL2024

ROAST: Review-level Opinion Aspect Sentiment Target Joint Detection for ABSA

Siva Uday Sampreeth Chebolu, Franck Dernoncourt, Nedim Lipka +1

Aspect-Based Sentiment Analysis (ABSA) has experienced tremendous expansion and diversity due to various shared tasks spanning several languages and fields and organized via SemEva…