3 citations · 3 across the 10 of their papers we have counts for
6 papers · 1 filter
Emotions Where Art Thou: Understanding and Characterizing the Emotional Latent Space of Large Language Models
Benjamin Reichman, Adar Avsian, Larry Heck
This work investigates how large language models (LLMs) internally represent emotion by analyzing the geometry of their hidden-state space. The paper identifies a low-dimensional e…
Outside Knowledge Conversational Video (OKCV) Dataset -- Dialoguing over Videos
Benjamin Reichman, Constantin Patsch, Jack Truxal +2
In outside knowledge visual question answering (OK-VQA), the model must identify relevant visual information within an image and incorporate external knowledge to accurately respon…
SensorQA: A Question Answering Benchmark for Daily-Life Monitoring
Benjamin Reichman, Xiaofan Yu, Lanxiang Hu +5
With the rapid growth in sensor data, effectively interpreting and interfacing with these data in a human-understandable way has become crucial. While existing research primarily f…
SensorChat: Answering Qualitative and Quantitative Questions during Long-Term Multimodal Sensor Interactions
Xiaofan Yu, Lanxiang Hu, Benjamin Reichman +5
Natural language interaction with sensing systems is crucial for addressing users' personal concerns and providing health-related insights into their daily lives. When a user asks…
"Yeah Right!" -- Do LLMs Exhibit Multimodal Feature Transfer?
Benjamin Reichman, Kartik Talamadupula
Human communication is a multifaceted and multimodal skill. Communication requires an understanding of both the surface-level textual content and the connotative intent of a piece…
Reading with Intent -- Neutralizing Intent
Benjamin Reichman, Adar Avsian, Larry Heck
Queries to large language models (LLMs) can be divided into two parts: the instruction/question and the accompanying context. The context for retrieval-augmented generation (RAG) s…