3 citations · 4 across the 9 of their papers we have counts for
10 papers
Lost with a Map: Conversational State and Behavioral Reliability in Language Models
Atahan Dokme, Larry Heck
Task-oriented dialogue requires maintaining and updating information across turns, yet language models expose no explicit belief-state object. We study how conversational state is…
Selective State-Space Adaptation and Retrieval for Language Model Reasoning
Atahan Dokme, Larry Heck
Low-rank adaptation introduces a static learned update applied identically to every input. The update provides task-level adaptation but does not explicitly represent token-level o…
Latent-IM: Latent Interaction Management for Speech LLMs
Adar Avsian, Atahan Dokme, Tony Woo +1
Classical spoken dialogue systems often separated dialogue management from response realization: a policy selected the next dialogue action, and a generation component expressed th…
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…
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…
Are Human Conversations Special? A Large Language Model Perspective
Toshish Jawale, Chaitanya Animesh, Sekhar Vallath +2
This study analyzes changes in the attention mechanisms of large language models (LLMs) when used to understand natural conversations between humans (human-human). We analyze three…
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…