6 citations · 14 across the 11 of their papers we have counts for
13 papers
Interpretable by Design: Query-Specific Neural Modules for Explainable Reinforcement Learning
Mehrdad Zakershahrak
Reinforcement learning has traditionally focused on a singular objective: learning policies that select actions to maximize reward. We challenge this paradigm by asking: what if we…
H-Net++: Hierarchical Dynamic Chunking for Tokenizer-Free Language Modelling in Morphologically-Rich Languages
Mehrdad Zakershahrak, Samira Ghodratnama
Byte-level language models eliminate fragile tokenizers but face computational challenges in morphologically-rich languages (MRLs), where words span many bytes. We propose H-NET++,…
Explanation, Debate, Align: A Weak-to-Strong Framework for Language Model Generalization
Mehrdad Zakershahrak, Samira Ghodratnama
The rapid advancement of artificial intelligence systems has brought the challenge of AI alignment to the forefront of research, particularly in complex decision-making and task ex…
SumRecom: A Personalized Summarization Approach by Learning from Users' Feedback
Samira Ghodratnama, Mehrdad Zakershahrak
Existing multi-document summarization approaches produce a uniform summary for all users without considering individuals' interests, which is highly impractical. Making a user-spec…
Adapting LLMs for Efficient, Personalized Information Retrieval: Methods and Implications
Samira Ghodratnama, Mehrdad Zakershahrak
The advent of Large Language Models (LLMs) heralds a pivotal shift in online user interactions with information. Traditional Information Retrieval (IR) systems primarily relied on…
A Personalized Reinforcement Learning Summarization Service for Learning Structure from Unstructured Data
Samira Ghodratnama, Amin Beheshti, Mehrdad Zakershahrak
The exponential growth of textual data has created a crucial need for tools that assist users in extracting meaningful insights. Traditional document summarization approaches often…