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20242026
most citedSharif-MGTD at SemEval-2024 Task 8: A Transformer-Based Approach to Detect Machine Generated Text

2 citations · 5 across the 10 of their papers we have counts for

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cs.CL2026

Last Translation Benchmark

Vilém Zouhar, Niyati Bafna, Mukund Choudhary +263

For scientific progress, we need benchmarks that test the limits of state-of-the-art models, and evaluation methods that inform us about failure cases. As models get stronger, stan…

cs.CL2026

The Trust Paradox: How CS Researchers Engage LLM Leaderboards

Pouya Sadeghi, Anamaria Crisan, Jimmy Lin

Large language model (LLM) leaderboards rank AI models using standardized benchmarks and have become highly visible across computer science, despite known limitations in their reli…

cs.CL2025

Extending LLMs to New Languages: A Case Study of Llama and Persian Adaptation

Samin Mahdizadeh Sani, Pouya Sadeghi, Thuy-Trang Vu +2

Large language models (LLMs) have made great progress in classification and text generation tasks. However, they are mainly trained on English data and often struggle with low-reso…

cs.CL2024★ 2 cited

Sharif-MGTD at SemEval-2024 Task 8: A Transformer-Based Approach to Detect Machine Generated Text

Seyedeh Fatemeh Ebrahimi, Karim Akhavan Azari, Amirmasoud Iravani +4

Detecting Machine-Generated Text (MGT) has emerged as a significant area of study within Natural Language Processing. While language models generate text, they often leave discerni…

cs.CL2024

SLPL SHROOM at SemEval2024 Task 06: A comprehensive study on models ability to detect hallucination

Pouya Fallah, Soroush Gooran, Mohammad Jafarinasab +5

Language models, particularly generative models, are susceptible to hallucinations, generating outputs that contradict factual knowledge or the source text. This study explores met…

cs.CL2024

uTeBC-NLP at SemEval-2024 Task 9: Can LLMs be Lateral Thinkers?

Pouya Sadeghi, Amirhossein Abaskohi, Yadollah Yaghoobzadeh

Inspired by human cognition, Jiang et al.(2023c) create a benchmark for assessing LLMs' lateral thinking-thinking outside the box. Building upon this benchmark, we investigate how…

cs.CL2024★ 2 cited

Benchmarking Large Language Models for Persian: A Preliminary Study Focusing on ChatGPT

Amirhossein Abaskohi, Sara Baruni, Mostafa Masoudi +9

This paper explores the efficacy of large language models (LLMs) for Persian. While ChatGPT and consequent LLMs have shown remarkable performance in English, their efficiency for m…