27 citations · 91 across the 55 of their papers we have counts for
29 papers · 1 filter
CAVEWOMAN: How Large Language Models Behave Under Linguistic Input and Output Compression
Morayo Danielle Adeyemi, Ryan A. Rossi, Franck Dernoncourt
"Talk short. Drop grammar. Save token." This caveman style is widely promoted as a way to cut inference cost, but whether it actually saves anything depends on which channel (the u…
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…
Cluster-R1: Large Reasoning Models Are Instruction-following Clustering Agents
Peijun Qing, Puneet Mathur, Nedim Lipka +5
General-purpose embedding models excel at recognizing semantic similarities but fail to capture the characteristics of texts specified by user instructions. In contrast, instructio…
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,…
Structured Uncertainty guided Clarification for LLM Agents
Manan Suri, Puneet Mathur, Nedim Lipka +3
LLM agents with tool-calling capabilities often fail when user instructions are ambiguous or incomplete, leading to incorrect invocations and task failures. Existing approaches ope…
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…
Steering MoE LLMs via Expert (De)Activation
Mohsen Fayyaz, Ali Modarressi, Hanieh Deilamsalehy +5
Mixture-of-Experts (MoE) in Large Language Models (LLMs) routes each token through a subset of specialized Feed-Forward Networks (FFN), known as experts. We present SteerMoE, a fra…
Lizard: An Efficient Linearization Framework for Large Language Models
Chien Van Nguyen, Huy Nguyen, Ruiyi Zhang +10
We propose Lizard, a linearization framework that transforms pretrained Transformer-based Large Language Models (LLMs) into subquadratic architectures. Transformers faces severe co…
Follow the Flow: Fine-grained Flowchart Attribution with Neurosymbolic Agents
Manan Suri, Puneet Mathur, Nedim Lipka +4
Flowcharts are a critical tool for visualizing decision-making processes. However, their non-linear structure and complex visual-textual relationships make it challenging to interp…
Quantitative LLM Judges
Aishwarya Sahoo, Jeevana Kruthi Karnuthala, Tushar Parmanand Budhwani +9
LLM-as-a-judge is a framework where a large language model (LLM) evaluates the output of another LLM. While LLMs excel at producing qualitative textual evaluations, they often stru…
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…
ChartLens: Fine-grained Visual Attribution in Charts
Manan Suri, Puneet Mathur, Nedim Lipka +3
The growing capabilities of multimodal large language models (MLLMs) have advanced tasks like chart understanding. However, these models often suffer from hallucinations, where gen…
Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey
Bo Ni, Zheyuan Liu, Leyao Wang +17
Retrieval-Augmented Generation (RAG) is an advanced technique designed to address the challenges of Artificial Intelligence-Generated Content (AIGC). By integrating context retriev…
ChartCitor: Multi-Agent Framework for Fine-Grained Chart Visual Attribution
Kanika Goswami, Puneet Mathur, Ryan Rossi +1
Large Language Models (LLMs) can perform chart question-answering tasks but often generate unverified hallucinated responses. Existing answer attribution methods struggle to ground…
PlotGen: Multi-Agent LLM-based Scientific Data Visualization via Multimodal Feedback
Kanika Goswami, Puneet Mathur, Ryan Rossi +1
Scientific data visualization is pivotal for transforming raw data into comprehensible visual representations, enabling pattern recognition, forecasting, and the presentation of da…
NoLiMa: Long-Context Evaluation Beyond Literal Matching
Ali Modarressi, Hanieh Deilamsalehy, Franck Dernoncourt +4
Recent large language models (LLMs) support long contexts ranging from 128K to 1M tokens. A popular method for evaluating these capabilities is the needle-in-a-haystack (NIAH) test…
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…
LUSIFER: Language Universal Space Integration for Enhanced Multilingual Embeddings with Large Language Models
Hieu Man, Nghia Trung Ngo, Viet Dac Lai +3
Recent advancements in large language models (LLMs) based embedding models have established new state-of-the-art benchmarks for text embedding tasks, particularly in dense vector-b…
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…
VisDoM: Multi-Document QA with Visually Rich Elements Using Multimodal Retrieval-Augmented Generation
Manan Suri, Puneet Mathur, Franck Dernoncourt +3
Understanding information from a collection of multiple documents, particularly those with visually rich elements, is important for document-grounded question answering. This paper…
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…
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…
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…
Taipan: Efficient and Expressive State Space Language Models with Selective Attention
Chien Van Nguyen, Huy Huu Nguyen, Thang M. Pham +8
Efficient long-context language modeling remains a significant challenge in Natural Language Processing (NLP). While Transformers dominate language tasks, they struggle with long s…