33 citations · 41 across the 15 of their papers we have counts for
17 papers · 1 filter
PARSA-Bench: A Comprehensive Persian Audio-Language Model Benchmark
Mohammad Javad Ranjbar Kalahroodi, Mohammad Amini, Parmis Bathayan +2
Persian poses unique audio understanding challenges through its classical poetry, traditional music, and pervasive code-switching, none of which is captured by existing benchmarks.…
PersianPunc: A Large-Scale Dataset and BERT-Based Approach for Persian Punctuation Restoration
Mohammad Javad Ranjbar Kalahroodi, Heshaam Faili, Azadeh Shakery
Punctuation restoration is essential for improving the readability and downstream utility of automatic speech recognition (ASR) outputs, yet remains underexplored for Persian despi…
SearchInstruct: Enhancing Domain Adaptation via Retrieval-Based Instruction Dataset Creation
Iman Barati, Mostafa Amiri, Heshaam Faili
Supervised Fine-Tuning (SFT) is essential for training large language models (LLMs), significantly enhancing critical capabilities such as instruction following and in-context lear…
PersianMedQA: Evaluating Large Language Models on a Persian-English Bilingual Medical Question Answering Benchmark
Mohammad Javad Ranjbar Kalahroodi, Amirhossein Sheikholselami, Sepehr Karimi +3
Large Language Models (LLMs) have achieved remarkable performance on a wide range of Natural Language Processing (NLP) benchmarks, often surpassing human-level accuracy. However, t…
SchemaGraphSQL: Efficient Schema Linking with Pathfinding Graph Algorithms for Text-to-SQL on Large-Scale Databases
AmirHossein Safdarian, Milad Mohammadi, Ehsan Jahanbakhsh +2
Text-to-SQL systems translate natural language questions into executable SQL queries, and recent progress with large language models (LLMs) has driven substantial improvements in t…
DeepQuestion: Systematic Generation of Real-World Challenges for Evaluating LLMs Performance
Ali Khoramfar, Ali Ramezani, Mohammad Mahdi Mohajeri +3
While Large Language Models (LLMs) achieve near-human performance on standard benchmarks, their capabilities often fail to generalize to complex, real-world problems. To bridge thi…