1 citations · 4 across the 13 of their papers we have counts for
16 papers
Understanding Communication Failures in Multi-Agent LLM Systems
Priya Pitre, Jun Hou, Yi Fang +2
Multi-agent LLM systems aim to improve reasoning through debate, evidence sharing, and mutual correction, but interaction can also introduce failures that final accuracy alone cann…
DataWeave: Deploying Human-LLM Analytics for Exploratory Structured Data Analysis
Raquib Bin Yousuf, Harith Laxman, Vitaliy Shkremetko +10
Data journalism, the practice of using data analysis to surface newsworthy stories, depends increasingly on the ability of reporters and investigative journalists to uncover trends…
Understanding Student Use of Large Language Models Across Computer Science Subfields
Sehrish Basir Nizamani, Yoonje Lee, Nikitha Donekal Chandrashekar +2
This research full paper examines how undergraduate students use large language models (LLMs) across computer science subfields. As LLMs become increasingly integrated into computi…
Skepticism vs. Convenience: Physics Students' Perceptions and Use of Large Language Models Before and After Instruction
Jake O'Brien, Margaret Ellis, Alma Robinson +2
The recent emergence of large language models (LLMs) has produced research focusing on the ability of these tools to solve physics problems, evaluate student work, or otherwise imp…
Adaptive Graph-of-Islands Evolution for Automatic Feature Engineering with LLMs
Sha Li, Naren Ramakrishnan
Automatic feature engineering (AutoFE) for tabular data requires discovering informative transformations from a large program space. Existing approaches suffer from three limitatio…
Demystify, Use, Reflect, Assess (DURA): An Experience Report on LLM Integration in CS2
Margaret Ellis, Nikitha Donekal Chandrashekar, Sehrish Basir Nizamani +3
Student access to Large Language Models (LLMs) is reshaping learning behaviors; at the same time students are entering the workforce where effective LLM use is becoming an expected…
Experience as a Compass: Multi-agent RAG with Evolving Orchestration and Agent Prompts
Sha Li, Naren Ramakrishnan
Multi-agent Retrieval-Augmented Generation (RAG), wherein each agent takes on a specific role, supports hard queries that require multiple steps and sources, or complex reasoning.…
RAG without Forgetting: Continual Query-Infused Key Memory
Yuntong Hu, Sha Li, Naren Ramakrishnan +1
Retrieval-augmented generation (RAG) systems commonly improve robustness via query-time adaptations such as query expansion and iterative retrieval. While effective, these approach…
Exploring LLMs for Scientific Information Extraction Using The SciEx Framework
Sha Li, Ayush Sadekar, Nathan Self +10
Large language models (LLMs) are increasingly touted as powerful tools for automating scientific information extraction. However, existing methods and tools often struggle with the…
Utilizing Metadata for Better Retrieval-Augmented Generation
Raquib Bin Yousuf, Shengzhe Xu, Mandar Sharma +3
Retrieval-Augmented Generation systems depend on retrieving semantically relevant document chunks to support accurate, grounded outputs from large language models. In structured an…
Hindsight is 20/20: Building Agent Memory that Retains, Recalls, and Reflects
Chris Latimer, Nicoló Boschi, Andrew Neeser +4
Agent memory has been touted as a dimension of growth for LLM-based applications, enabling agents that can accumulate experience, adapt across sessions, and move beyond single-shot…
Demystify, Use, Reflect: Preparing students to be informed LLM-users
Nikitha Donekal Chandrashekar, Sehrish Basir Nizamani, Margaret Ellis +1
We transitioned our post-CS1 course that introduces various subfields of computer science so that it integrates Large Language Models (LLMs) in a structured, critical, and practica…
LLMs as Layout Designers: Enhanced Spatial Reasoning for Content-Aware Layout Generation
Sha Li, Stefano Petrangeli, Yu Shen +2
While Large Language Models (LLMs) have demonstrated impressive reasoning and planning abilities in textual domains and can effectively follow instructions for complex tasks, their…
QuOTE: Question-Oriented Text Embeddings
Andrew Neeser, Kaylen Latimer, Aadyant Khatri +2
We present QuOTE (Question-Oriented Text Embeddings), a novel enhancement to retrieval-augmented generation (RAG) systems, aimed at improving document representation for accurate a…
Oreo: A Plug-in Context Reconstructor to Enhance Retrieval-Augmented Generation
Sha Li, Naren Ramakrishnan
Retrieval-Augmented Generation (RAG) aims to augment the capabilities of Large Language Models (LLMs) by retrieving and incorporate external documents or chunks prior to generation…
Steering LLM Summarization with Visual Workspaces for Sensemaking
Xuxin Tang, Eric Krokos, Can Liu +4
Large Language Models (LLMs) have been widely applied in summarization due to their speedy and high-quality text generation. Summarization for sensemaking involves information comp…