@inproceedings{fenogenova-etal-2024-mera,
title = "{MERA}: A Comprehensive {LLM} Evaluation in {R}ussian",
author = "Fenogenova, Alena and
Chervyakov, Artem and
Martynov, Nikita and
Kozlova, Anastasia and
Tikhonova, Maria and
Akhmetgareeva, Albina and
Emelyanov, Anton and
Shevelev, Denis and
Lebedev, Pavel and
Sinev, Leonid and
Isaeva, Ulyana and
Kolomeytseva, Katerina and
Moskovskiy, Daniil and
Goncharova, Elizaveta and
Savushkin, Nikita and
Mikhailova, Polina and
Minaeva, Anastasia and
Dimitrov, Denis and
Panchenko, Alexander and
Markov, Sergey",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.534",
doi = "10.18653/v1/2024.acl-long.534",
pages = "9920--9948",
abstract = "Over the past few years, one of the most notable advancements in AI research has been in foundation models (FMs), headlined by the rise of language models (LMs). However, despite researchers{'} attention and the rapid growth in LM application, the capabilities, limitations, and associated risks still need to be better understood. To address these issues, we introduce a new instruction benchmark, MERA, oriented towards the FMs{'} performance on the Russian language. The benchmark encompasses 21 evaluation tasks for generative models covering 10 skills and is supplied with private answer scoring to prevent data leakage. The paper introduces a methodology to evaluate FMs and LMs in fixed zero- and few-shot instruction settings that can be extended to other modalities. We propose an evaluation methodology, an open-source code base for the MERA assessment, and a leaderboard with a submission system. We evaluate open LMs as baselines and find they are still far behind the human level. We publicly release MERA to guide forthcoming research, anticipate groundbreaking model features, standardize the evaluation procedure, and address potential ethical concerns and drawbacks.",
}
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<abstract>Over the past few years, one of the most notable advancements in AI research has been in foundation models (FMs), headlined by the rise of language models (LMs). However, despite researchers’ attention and the rapid growth in LM application, the capabilities, limitations, and associated risks still need to be better understood. To address these issues, we introduce a new instruction benchmark, MERA, oriented towards the FMs’ performance on the Russian language. The benchmark encompasses 21 evaluation tasks for generative models covering 10 skills and is supplied with private answer scoring to prevent data leakage. The paper introduces a methodology to evaluate FMs and LMs in fixed zero- and few-shot instruction settings that can be extended to other modalities. We propose an evaluation methodology, an open-source code base for the MERA assessment, and a leaderboard with a submission system. We evaluate open LMs as baselines and find they are still far behind the human level. We publicly release MERA to guide forthcoming research, anticipate groundbreaking model features, standardize the evaluation procedure, and address potential ethical concerns and drawbacks.</abstract>
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%0 Conference Proceedings
%T MERA: A Comprehensive LLM Evaluation in Russian
%A Fenogenova, Alena
%A Chervyakov, Artem
%A Martynov, Nikita
%A Kozlova, Anastasia
%A Tikhonova, Maria
%A Akhmetgareeva, Albina
%A Emelyanov, Anton
%A Shevelev, Denis
%A Lebedev, Pavel
%A Sinev, Leonid
%A Isaeva, Ulyana
%A Kolomeytseva, Katerina
%A Moskovskiy, Daniil
%A Goncharova, Elizaveta
%A Savushkin, Nikita
%A Mikhailova, Polina
%A Minaeva, Anastasia
%A Dimitrov, Denis
%A Panchenko, Alexander
%A Markov, Sergey
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F fenogenova-etal-2024-mera
%X Over the past few years, one of the most notable advancements in AI research has been in foundation models (FMs), headlined by the rise of language models (LMs). However, despite researchers’ attention and the rapid growth in LM application, the capabilities, limitations, and associated risks still need to be better understood. To address these issues, we introduce a new instruction benchmark, MERA, oriented towards the FMs’ performance on the Russian language. The benchmark encompasses 21 evaluation tasks for generative models covering 10 skills and is supplied with private answer scoring to prevent data leakage. The paper introduces a methodology to evaluate FMs and LMs in fixed zero- and few-shot instruction settings that can be extended to other modalities. We propose an evaluation methodology, an open-source code base for the MERA assessment, and a leaderboard with a submission system. We evaluate open LMs as baselines and find they are still far behind the human level. We publicly release MERA to guide forthcoming research, anticipate groundbreaking model features, standardize the evaluation procedure, and address potential ethical concerns and drawbacks.
%R 10.18653/v1/2024.acl-long.534
%U https://aclanthology.org/2024.acl-long.534
%U https://doi.org/10.18653/v1/2024.acl-long.534
%P 9920-9948
Markdown (Informal)
[MERA: A Comprehensive LLM Evaluation in Russian](https://aclanthology.org/2024.acl-long.534) (Fenogenova et al., ACL 2024)
ACL
- Alena Fenogenova, Artem Chervyakov, Nikita Martynov, Anastasia Kozlova, Maria Tikhonova, Albina Akhmetgareeva, Anton Emelyanov, Denis Shevelev, Pavel Lebedev, Leonid Sinev, Ulyana Isaeva, Katerina Kolomeytseva, Daniil Moskovskiy, Elizaveta Goncharova, Nikita Savushkin, Polina Mikhailova, Anastasia Minaeva, Denis Dimitrov, Alexander Panchenko, et al.. 2024. MERA: A Comprehensive LLM Evaluation in Russian. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 9920–9948, Bangkok, Thailand. Association for Computational Linguistics.