8 citations · 13 across the 11 of their papers we have counts for
13 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…
Security and Privacy Taxonomy Generation from Mobile App Reviews
Moghis Fereidouni, Vinaik Chhetri, Krishna Upadhyay +2
Mobile app reviews are a rich, continuously renewing source of how users experience privacy and security, yet existing taxonomies of these concerns are hand-crafted and cannot keep…
Understanding Robustness of Model Editing in Code LLMs
Vinaik Chhetri, Moghis Fereidouni, A. B Siddique +1
Large language models (LLMs) for code are increasingly used in software development, but they remain static after pretraining while APIs and software libraries continue to evolve.…
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…
Toward Open-domain Slot Filling via Self-supervised Co-training
Adib Mosharrof, Moghis Fereidouni, A. B. Siddique
Slot filling is one of the critical tasks in modern conversational systems. The majority of existing literature employs supervised learning methods, which require labeled training…
Proactive Prioritization of App Issues via Contrastive Learning
Moghis Fereidouni, Adib Mosharrof, Umar Farooq +1
Mobile app stores produce a tremendous amount of data in the form of user reviews, which is a huge source of user requirements and sentiments; such reviews allow app developers to…