4 citations · 7 across the 6 of their papers we have counts for
13 papers · 1 filter
Persistent Personas? Role-Playing, Instruction Following, and Safety in Extended Interactions
Pedro Henrique Luz de Araujo, Michael A. Hedderich, Ali Modarressi +2
Persona-assigned large language models (LLMs) are used in domains such as education, healthcare, and sociodemographic simulation. Yet, they are typically evaluated only in short, s…
Steering MoE LLMs via Expert (De)Activation
Mohsen Fayyaz, Ali Modarressi, Hanieh Deilamsalehy +5
Mixture-of-Experts (MoE) in Large Language Models (LLMs) routes each token through a subset of specialized Feed-Forward Networks (FFN), known as experts. We present SteerMoE, a fra…
Time Course MechInterp: Analyzing the Evolution of Components and Knowledge in Large Language Models
Ahmad Dawar Hakimi, Ali Modarressi, Philipp Wicke +1
Understanding how large language models (LLMs) acquire and store factual knowledge is crucial for enhancing their interpretability and reliability. In this work, we analyze the evo…
ImpliRet: Benchmarking the Implicit Fact Retrieval Challenge
Zeinab Sadat Taghavi, Ali Modarressi, Yunpu Ma +1
Retrieval systems are central to many NLP pipelines, but often rely on surface-level cues such as keyword overlap and lexical semantic similarity. To evaluate retrieval beyond thes…
Do We Know What LLMs Don't Know? A Study of Consistency in Knowledge Probing
Raoyuan Zhao, Abdullatif Köksal, Ali Modarressi +2
The reliability of large language models (LLMs) is greatly compromised by their tendency to hallucinate, underscoring the need for precise identification of knowledge gaps within L…
Collapse of Dense Retrievers: Short, Early, and Literal Biases Outranking Factual Evidence
Mohsen Fayyaz, Ali Modarressi, Hinrich Schuetze +1
Dense retrieval models are commonly used in Information Retrieval (IR) applications, such as Retrieval-Augmented Generation (RAG). Since they often serve as the first step in these…