1 citations · 4 across the 13 of their papers we have counts for
5 papers · 1 filter
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…