anthology-assist
commented
5 months ago @firojalam Could you highlight missing or incorrect author names?
Closed firojalam closed 3 weeks ago
anthology-assist
commented
5 months ago @firojalam Could you highlight missing or incorrect author names?
firojalam
commented
5 months ago @anthology-assist , The following authors name are missing:
Hawasly, Majd and Durrani, Nadir and Alam, Firoj
Thank you
mjpost
commented
3 months ago This correction was made to the wrong paper. Can we please carefully check the author lists for the following two papers?
firojalam
commented
3 months ago Dear Matt, Thanks for your email. Unfortunately, now bibs are incorrect for both of our papers.
Regards Firoj
................ Firoj Alam, PhD http://sites.google.com/site/firojalam/
On Thu, 4 Jul 2024 at 5:21 AM Matt Post @.***> wrote:
This correction was made to the wrong paper. Can we please carefully check the author lists for the following two papers?
— Reply to this email directly, view it on GitHub https://github.com/acl-org/acl-anthology/issues/3177#issuecomment-2207880511, or unsubscribe https://github.com/notifications/unsubscribe-auth/ABABTSDJGQI5NK2KTGWO2CLZKSWQ7AVCNFSM6AAAAABFTKXB5SVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDEMBXHA4DANJRGE . You are receiving this because you were mentioned.Message ID: @.***>
anthology-assist
commented
3 weeks ago This issue has been solved.
Confirm that this is a metadata correction
Anthology ID
2024.eacl-demo.23
Type of Paper Metadata Correction
Correction to Paper Title
No response
Correction to Paper Abstract
No response
Correction to Author Name(s)
@inproceedings{dalvi-etal-2024-llmebench, title = "{LLM}e{B}ench: A Flexible Framework for Accelerating {LLM}s Benchmarking", author = "Dalvi, Fahim and Hasanain, Maram and Boughorbel, Sabri and Mousi, Basel and Abdaljalil, Samir and Nazar, Nizi and Abdelali, Ahmed and Chowdhury, Shammur Absar and Mubarak, Hamdy and Ali, Ahmed and Hawasly, Majd and Durrani, Nadir and Alam, Firoj", editor = "Aletras, Nikolaos and De Clercq, Orphee", booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations", month = mar, year = "2024", address = "St. Julians, Malta", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.eacl-demo.23", pages = "214--222", abstract = "The recent development and success of Large Language Models (LLMs) necessitate an evaluation of their performance across diverse NLP tasks in different languages. Although several frameworks have been developed and made publicly available, their customization capabilities for specific tasks and datasets are often complex for different users. In this study, we introduce the LLMeBench framework, which can be seamlessly customized to evaluate LLMs for any NLP task, regardless of language. The framework features generic dataset loaders, several model providers, and pre-implements most standard evaluation metrics. It supports in-context learning with zero- and few-shot settings. A specific dataset and task can be evaluated for a given LLM in less than 20 lines of code while allowing full flexibility to extend the framework for custom datasets, models, or tasks. The framework has been tested on 31 unique NLP tasks using 53 publicly available datasets within 90 experimental setups, involving approximately 296K data points. We open-sourced LLMeBench for the community (https://github.com/qcri/LLMeBench/) and a video demonstrating the framework is available online (https://youtu.be/9cC2m{_}abk3A)).", }