2 citations · 5 across the 10 of their papers we have counts for
12 papers
Query-adaptive Recurrent Tokenization
Bavesh Balaji, Pranav Gopinath, Pouya Sadeghi +1
Visual information worth extracting from an image and the token budget it warrants depend on the text query. Yet modern vision and vision-language models largely encode images inde…
How Far Can INRs Go? Cross-Domain Parameter-efficient INR-Based Semantic Segmentation for Brain MRI
Ziyao Shang, Pouya Sadeghi, Letian Jiang +2
Biomedical image segmentation is central to medical image analysis, but practical deployment often faces limited annotations, memory constraints, and cross-site distribution shifts…
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…
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…
PinPoint: Prompting with Informative Interior Points
Pouya Sadeghi, Shawn He, Pedro Pablo Guerrero Vela +3
Modern referring image segmentation pipelines couple a vision-language model (VLM) for grounding with a promptable segmenter such as the Segment Anything Model (SAM) for mask gener…
Who Evaluates AI's Social Impacts? Mapping Coverage and Gaps in First and Third Party Evaluations
Anka Reuel, Avijit Ghosh, Jenny Chim +32
Foundation models are increasingly central to high-stakes AI systems, and governance frameworks now depend on evaluations to assess their risks and capabilities. Although general c…
Supermartingale Certificates for Quantitative Omega-regular Verification and Control
Thomas A. Henzinger, Kaushik Mallik, Pouya Sadeghi +1
We present the first supermartingale certificate for quantitative -regular properties of discrete-time infinite-state stochastic systems. Our certificate is defined on the produ…
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…
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…
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…
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…
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…