Thailand

This paper introduces the Open Ko-LLM Leaderboard and the Ko-H5 Benchmark as vital tools for evaluating Large Language Models (LLMs) in Korean. Incorporating private test sets while mirroring the English Open LLM Leaderboard, we establish a robust evaluation framework that has been well integrated in the Korean LLM community. We perform data leakage analysis that shows the benefit of private test sets along with a correlation study within the Ko-H5 benchmark and temporal analyses of the Ko-H5 score. Moreover, we present empirical support for the need to expand beyond set benchmarks. We hope the Open Ko-LLM Leaderboard sets precedent for expanding LLM evaluation to foster more linguistic diversity.

ACL 2024

Open Ko-LLM Leaderboard: Evaluating Large Language Models in Korean with Ko-H5 Benchmark

ko-h5 benchmark

open ko-llm leaderboard

large language models

poster

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The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

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Relying on the Unreliable: The Impact of Language Models’ Reluctance to Express Uncertainty

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In­-context Mixing (ICM): Code­-mixed Prompts for Multilingual LLMs

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Disinformation Capabilities of Large Language Models

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Cendol: Open Instruction-tuned Generative Large Language Models for Indonesian Languages

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Latxa: An Open Language Model and Evaluation Suite for Basque

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The Echoes of Multilinguality: Tracing Cultural Value Shifts during Language Model Fine-tuning

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Relying on the Unreliable: The Impact of Language Models’ Reluctance to Express Uncertainty

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