2 citations · 4 across the 9 of their papers we have counts for
11 papers
On Device Agentic Operation Caches -- Classifier-Centric NL-to-Action Generation
Moghis Fereidouni, Anthony Arnold, Sumit Gulwani +2
Agentic AI is increasingly being embedded in software applications to provide natural language interfaces to features and functionality. In most cases these agents are powered by e…
GAPS: Dimension-Level Gates for Conditional Activation Steering
Moghis Fereidouni, Muhammad Umair Haider, Hassan Sajjad +1
Activation steering suppresses undesired behaviors in language models by adding a steering vector to the hidden state during generation. Recent conditional methods such as CAST and…
Persona-Guided LLM Agents for Task-Oriented Dialogue
Maryam Shoaeinaeini, Brent Harrison, A. B. Siddique
Prior work has shown that large language models can express diverse personality traits in open-ended text generation, but it is unclear whether they can do so in goal-directed dial…
LangFIR: Discovering Sparse Language-Specific Features from Monolingual Data for Language Steering
Sing Hieng Wong, Hassan Sajjad, A. B. Siddique
Large language models (LLMs) show strong multilingual capabilities, yet reliably controlling the language of their outputs remains difficult. Representation-level steering addresse…
Evaluating Sparse Autoencoders for Monosemantic Representation
Moghis Fereidouni, Muhammad Umair Haider, Peizhong Ju +1
A key barrier to interpreting large language models is polysemanticity, where neurons activate for multiple unrelated concepts. Sparse autoencoders (SAEs) have been proposed to mit…
A Framework for Generating Conversational Recommendation Datasets from Behavioral Interactions
Vinaik Chhetri, Yousaf Reza, Moghis Fereidouni +3
Modern recommendation systems typically follow two complementary paradigms: collaborative filtering, which models long-term user preferences from historical interactions, and conve…
INTERPOS: Interaction Rhythm Guided Positional Morphing for Mobile App Recommender Systems
M. H. Maqbool, Moghis Fereidouni, Umar Farooq +2
The mobile app market has expanded exponentially, offering millions of apps with diverse functionalities, yet research in mobile app recommendation remains limited. Traditional seq…
Evaluating and Enhancing Out-of-Domain Generalization of Task-Oriented Dialog Systems for Task Completion without Turn-level Dialog Annotations
Adib Mosharrof, Moghis Fereidouni, A. B. Siddique
Traditional task-oriented dialog (ToD) systems rely heavily on labor-intensive turn-level annotations, such as dialogue states and policy labels, for training. This work explores w…
Improving Multi-turn Task Completion in Task-Oriented Dialog Systems via Prompt Chaining and Fine-Grained Feedback
Moghis Fereidouni, Md Sajid Ahmed, Adib Mosharrof +1
Task-oriented dialog (TOD) systems facilitate users in accomplishing complex, multi-turn tasks through natural language. While instruction-tuned large language models (LLMs) have d…
MobileConvRec: A Conversational Dataset for Mobile Apps Recommendations
Srijata Maji, Moghis Fereidouni, Vinaik Chhetri +2
Existing recommendation systems have focused on two paradigms: 1- historical user-item interaction-based recommendations and 2- conversational recommendations. Conversational recom…
Grounded Language Agent for Product Search via Intelligent Web Interactions
Moghis Fereidouni, Adib Mosharrof, A. B. Siddique
The development of agents powered by large language models (LLMs) to accomplish complex high-level user intents, has attracted significant attention recently. However, employing LL…