From the 1 of 7 linked papers with an AI index.
7 papers
Latent-IM: Latent Interaction Management for Speech LLMs
Adar Avsian, Atahan Dokme, Tony Woo +1
The paper introduces Latent-IM, a framework that internally manages dialogue moves in speech‑based large language models by selecting and realizing conversational actions, achievin…
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…
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…
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…