3 citations · 3 across the 10 of their papers we have counts for
8 papers · 1 filter
TEMPER: Testing Emotional Perturbation in Quantitative Reasoning
Atahan Dokme, Benjamin Reichman, Larry Heck
Large language models are trained and evaluated on quantitative reasoning tasks written in clean, emotionally neutral language. However, real-world queries are often wrapped in fru…
Emotion is Not Just a Label: Latent Emotional Factors in LLM Processing
Benjamin Reichman, Adar Avsian, Samuel Webster +1
Large language models are routinely deployed on text that varies widely in emotional tone, yet their reasoning behavior is typically evaluated without accounting for emotion as a s…
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…
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…
"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…
Emotional RAG LLMs: Reading Comprehension for the Open Internet
Benjamin Reichman, Adar Avsian, Kartik Talamadupula +2
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…
Dense Passage Retrieval: Is it Retrieving?
Benjamin Reichman, Larry Heck
Dense passage retrieval (DPR) is the first step in the retrieval augmented generation (RAG) paradigm for improving the performance of large language models (LLM). DPR fine-tunes pr…