On Limitations of LLM as Annotator for Low Resource Languages

A survey on recent approaches for natural language processing in low-resource scenarios

Michael A. Hedderich, Lukas Lange, Heike Adel, Jannik Strotgen, and Dietrich Klakow. 2020 · 2020

Earlier work this paper cites.

L3cubemahasent: A marathi tweet-based sentiment analysis dataset

Atharva Kulkarni, Meet Mandhane, Manali Likhitkar, Gayatri Kshirsagar, and Raviraj Joshi. 2021 · 2021

Earlier work this paper cites.

Is gpt-3 a good data annotator?

Bosheng Ding, Chengwei Qin, Linlin Liu, Lidong Bing, Shafiq R. Joty, and Boyang Albert Li. 2022 · 2022

Earlier work this paper cites.

L3cube-mahanlp: Marathi natural language processing datasets, models, and library

Original

Raviraj Joshi. 2022 · 2022

Earlier work this paper cites.

L3cube-mahahate: A tweet-based marathi hate speech detection dataset and bert models

Hrushikesh Patil, Abhishek Velankar, and Raviraj Joshi. 2022 · 2022

Earlier work this paper cites.

Beyond the imitation game: Quantifying and extrapolating the capabilities of language models

Original

Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, et al. 2022 · 2022

Earlier work this paper cites.

Low-resource language processing using improved deep learning with hunter–prey optimization algorithm

Fahd N. Al-Wesabi, Hala J. Alshahrani, Azza Elneil Osman, and Elmouez Samir Abd Elhameed. 2023 · 2023

Earlier work this paper cites.

Human-like summarization evaluation with chatgpt

Original

Mingqi Gao, Jie Ruan, Renliang Sun, Xunjian Yin, Shiping Yang, and Xiaojun Wan. 2023 · 2023

Cited alongside, same era.

Chatgpt: beginning of an end of manual linguistic data annotation

Original

Taja Kuzman, Igor Mozetic, and Nikola Ljubešic. 2023 · 2023

Cited alongside, same era.

L3cube-mahanews: News-based short text and long document classification datasets in marathi

Saloni Mittal, Vidula Magdum, Sharayu Hiwarkhedkar, Omkar Dhekane, and Raviraj Joshi. 2023 · 2023

Cited alongside, same era.

Are large language models good annotators?

Jay Mohta, Kenan Emir Ak, Yan Xu, and Mingwei Shen. 2023 · 2023

Cited alongside, same era.

L3cube-mahasent-md: A multi-domain marathi sentiment analysis dataset and transformer models

Aabha Pingle, Aditya Vyawahare, Isha Joshi, Rahul Tangsali, and Raviraj Joshi. 2023 · 2023

Cited alongside, same era.

Llms instead of human judges? a large scale empirical study across 20 nlp evaluation tasks

Original

Anna Bavaresco, Raffaella Bernardi, Leonardo Bertolazzi, Desmond Elliott, Raquel Fern’andez, Albert Gatt, E. Ghaleb, Mario Giulianelli, Michael Hanna, Alexander Koller, Andr’e F. T. Martins, Philipp Mondorf, Vera Neplenbroek, Sandro Pezzelle, Barbara Plank, David Schlangen, Alessandro Suglia, Aditya K Surikuchi, Ece Takmaz, and Alberto Testoni. 2024 · 2024

Closest in time.

Llms in the loop: Leveraging large language model annotations for active learning in low-resource languages

Nataliia Kholodna, Sahib Julka, Mohammad Khodadadi, Muhammed Nurullah Gumus, and Michael Granitzer. 2024 · 2024

Closest in time.

A survey on challenges and advances in natural language processing with a focus on legal informatics and low-resource languages

Panteleimon Krasadakis, Evangelos Sakkopoulos, and Vassilios S. Verykios. 2024 · 2024

Closest in time.

L3cube-indicnews: News-based short text and long document classification datasets in indic languages

Original

Aishwarya Mirashi, Srushti Sonavane, Purva Lingayat, Tejas Padhiyar, and Raviraj Joshi. 2024 · 2024

Closest in time.