59 citations · 131 across the 35 of their papers we have counts for
35 papers
KnowSim: Evaluating Information Calibration in LLM Assistants with User Simulators that Learn
Yoonjoo Lee, Hyoungwook Jin, Tae Soo Kim +3
To effectively collaborate with users on knowledge-intensive tasks, Large Language Models (LLMs) must perform information calibration: matching content to a user's evolving underst…
Unifying Graph Neural Networks Through a Common Layer Equation
Sai Karthik Navuluru, Siddhartha Shankar Das, Bo Ni +9
Graph neural networks are commonly described through family-specific equations whose notation obscures shared computations and structural differences. We introduce a common layer e…
Personalized Auto-Research: Towards a True AI Co-Scientist
Bo Ni, Franck Dernoncourt, Hongjie Chen +5
AI co-scientists that generate hypotheses, retrieve related work, design experiments, execute code, and draft full papers are beginning to change how research is carried out. Despi…
TRACE: Trajectory Reasoning through Adaptive Cross-Step Evidence Aggregation for LLM Agents
Vijitha Mittapalli, Shreyaa Jayant Dani, Satya Srujana Pilli +7
Autonomous LLM agents can pursue hidden malicious objectives through sequences of individually benign actions, making sabotage difficult to detect using standard trajectory-level m…
Multimodal Music Recommendation System using LLMs
Srikar Prabhas Kandagatla, Sreehitha R. Narayana, Chandana Magapu +6
Music recommendation systems typically treat songs as opaque tokens, relying on collaborative interaction histories which overlooks semantic or acoustic content. Prior work has exp…
Sparse Personalized Text Generation with Multi-Trajectory Reasoning
Bo Ni, Haowei Fu, Qinwen Ge +10
As Large Language Models (LLMs) advance, personalization has become a key mechanism for tailoring outputs to individual user needs. However, most existing methods rely heavily on d…
A Survey on LLM-based Conversational User Simulation
Bo Ni, Leyao Wang, Yu Wang +27
User simulation has long played a vital role in computer science due to its potential to support a wide range of applications. Language, as the primary medium of human communicatio…
Blind to the Human Touch: Overlap Bias in LLM-Based Summary Evaluation
Jiangnan Fang, Cheng-Tse Liu, Hanieh Deilamsalehy +5
Large language model (LLM) judges have often been used alongside traditional, algorithm-based metrics for tasks like summarization because they better capture semantic information,…
Benchmarking Knowledge-Extraction Attack and Defense on Retrieval-Augmented Generation
Zhisheng Qi, Utkarsh Sahu, Li Ma +9
Retrieval-Augmented Generation (RAG) has become a cornerstone of knowledge-intensive applications, including enterprise chatbots, healthcare assistants, and agentic memory manageme…
Segment Length Matters: A Study of Segment Lengths on Audio Fingerprinting Performance
Ziling Gong, Yunyan Ouyang, Iram Kamdar +5
Audio fingerprinting provides an identifiable representation of acoustic signals, which can be later used for identification and retrieval systems. To obtain a discriminative repre…
Iterative Critique-Refine Framework for Enhancing LLM Personalization
Durga Prasad Maram, Dhruvin Gandhi, Zonghai Yao +5
Personalized text generation requires models not only to produce coherent text but also to align with a target user's style, tone, and topical focus. Existing retrieval-augmented a…
Towards Bridging Review Sparsity in Recommendation with Textual Edge Graph Representation
Leyao Wang, Xutao Mao, Xuhui Zhan +5
Textual reviews enrich recommender systems with fine-grained preference signals and enhanced explainability. However, in real-world scenarios, users rarely leave reviews, resulting…
Forecasting Time Series with LLMs via Patch-Based Prompting and Decomposition
Mayank Bumb, Anshul Vemulapalli, Sri Harsha Vardhan Prasad Jella +7
Recent advances in Large Language Models (LLMs) have demonstrated new possibilities for accurate and efficient time series analysis, but prior work often required heavy fine-tuning…
A Graph Perspective to Probe Structural Patterns of Knowledge in Large Language Models
Utkarsh Sahu, Zhisheng Qi, Yongjia Lei +6
Large language models have been extensively studied as neural knowledge bases for their knowledge access, editability, reasoning, and explainability. However, few works focus on th…
A Personalized Conversational Benchmark: Towards Simulating Personalized Conversations
Li Li, Peilin Cai, Ryan A. Rossi +21
We present PersonaConvBench, a large-scale benchmark for evaluating personalized reasoning and generation in multi-turn conversations with large language models (LLMs). Unlike exis…
From Selection to Generation: A Survey of LLM-based Active Learning
Yu Xia, Subhojyoti Mukherjee, Zhouhang Xie +31
Active Learning (AL) has been a powerful paradigm for improving model efficiency and performance by selecting the most informative data points for labeling and training. In recent…
Personalized Graph-Based Retrieval for Large Language Models
Steven Au, Cameron J. Dimacali, Ojasmitha Pedirappagari +7
As large language models (LLMs) evolve, their ability to deliver personalized and context-aware responses offers transformative potential for improving user experiences. Existing p…
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…
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…
GUI Agents: A Survey
Dang Nguyen, Jian Chen, Yu Wang +27
Graphical User Interface (GUI) agents, powered by Large Foundation Models, have emerged as a transformative approach to automating human-computer interaction. These agents autonomo…
GRS-QA -- Graph Reasoning-Structured Question Answering Dataset
Anish Pahilajani, Devasha Trivedi, Jincen Shuai +7
Large Language Models (LLMs) have excelled in multi-hop question-answering (M-QA) due to their advanced reasoning abilities. However, the impact of the inherent reasoning structure…
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…
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…
Large Generative Graph Models
Yu Wang, Ryan A. Rossi, Namyong Park +6
Large Generative Models (LGMs) such as GPT, Stable Diffusion, Sora, and Suno are trained on a huge amount of language corpus, images, videos, and audio that are extremely diverse f…