4 papers
Small Reasoning Models are Instruction Followers in Function Calling
Yalda Taheri, Mohammad Hassan Heydari, Erfan Naaman +1
Function calling represents the core capability of agentic large language models (LLMs). Existing research has focused on enhancing LLMs function-calling accuracy through fine-tuni…
Delta-Audit: Explaining What Changes When Models Change
Arshia Hemmat, Afsaneh Fatemi
Model updates (new hyperparameters, kernels, depths, solvers, or data) change performance, but the \emph{reason} often remains opaque. We introduce \textbf{Delta-Attribution} (\mbo…
Leveraging Retrieval-Augmented Generation for Persian University Knowledge Retrieval
Arshia Hemmat, Kianoosh Vadaei, Mohammad Hassan Heydari +1
This paper introduces an innovative approach using Retrieval-Augmented Generation (RAG) pipelines with Large Language Models (LLMs) to enhance information retrieval and query respo…
Context Awareness Gate For Retrieval Augmented Generation
Mohammad Hassan Heydari, Arshia Hemmat, Erfan Naman +1
Retrieval Augmented Generation (RAG) has emerged as a widely adopted approach to mitigate the limitations of large language models (LLMs) in answering domain-specific questions. Pr…