Representing the Under-Represented: Cultural and Core Capability Benchmarks for Developing Thai Large Language Models
- URL: http://arxiv.org/abs/2410.04795v2
- Date: Tue, 8 Oct 2024 04:05:18 GMT
- Title: Representing the Under-Represented: Cultural and Core Capability Benchmarks for Developing Thai Large Language Models
- Authors: Dahyun Kim, Sukyung Lee, Yungi Kim, Attapol Rutherford, Chanjun Park,
- Abstract summary: The rapid advancement of large language models (LLMs) has highlighted the need for robust evaluation frameworks.
We propose two key benchmarks: Thai-H6 and Thai Cultural and Linguistic Intelligence Benchmark (ThaiCLI)
- Score: 8.746788828655356
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The rapid advancement of large language models (LLMs) has highlighted the need for robust evaluation frameworks that assess their core capabilities, such as reasoning, knowledge, and commonsense, leading to the inception of certain widely-used benchmark suites such as the H6 benchmark. However, these benchmark suites are primarily built for the English language, and there exists a lack thereof for under-represented languages, in terms of LLM development, such as Thai. On the other hand, developing LLMs for Thai should also include enhancing the cultural understanding as well as core capabilities. To address these dual challenge in Thai LLM research, we propose two key benchmarks: Thai-H6 and Thai Cultural and Linguistic Intelligence Benchmark (ThaiCLI). Through a thorough evaluation of various LLMs with multi-lingual capabilities, we provide a comprehensive analysis of the proposed benchmarks and how they contribute to Thai LLM development. Furthermore, we will make both the datasets and evaluation code publicly available to encourage further research and development for Thai LLMs.
Related papers
- Understanding the Role of LLMs in Multimodal Evaluation Benchmarks [77.59035801244278]
This paper investigates the role of the Large Language Model (LLM) backbone in Multimodal Large Language Models (MLLMs) evaluation.
Our study encompasses four diverse MLLM benchmarks and eight state-of-the-art MLLMs.
Key findings reveal that some benchmarks allow high performance even without visual inputs and up to 50% of error rates can be attributed to insufficient world knowledge in the LLM backbone.
arXiv Detail & Related papers (2024-10-16T07:49:13Z) - Disce aut Deficere: Evaluating LLMs Proficiency on the INVALSI Italian Benchmark [12.729687989535359]
evaluating Large Language Models (LLMs) in languages other than English is crucial for ensuring their linguistic versatility, cultural relevance, and applicability in diverse global contexts.
We tackle this challenge by introducing a structured benchmark using the INVALSI tests, a set of well-established assessments designed to measure educational competencies across Italy.
arXiv Detail & Related papers (2024-06-25T13:20:08Z) - Thai Winograd Schemas: A Benchmark for Thai Commonsense Reasoning [0.0]
This research introduces a collection of Winograds in Thai, a novel dataset designed to evaluate commonsense reasoning capabilities in the context of the Thai language.
We evaluate the performance of popular large language models on this benchmark, revealing their strengths, limitations, and providing insights into the current state-of-the-art.
arXiv Detail & Related papers (2024-05-28T17:14:02Z) - Measuring Taiwanese Mandarin Language Understanding [24.581360653015423]
We present TMLU, a holistic evaluation suit tailored for assessing the advanced knowledge and reasoning capability in large language models (LLMs)
TMLU consists of an array of 37 subjects across social science, STEM, humanities, Taiwan-specific content, and others, ranging from middle school to professional levels.
arXiv Detail & Related papers (2024-03-29T13:56:21Z) - Pragmatic Competence Evaluation of Large Language Models for the Korean Language [0.6757476692230009]
This study evaluates how well Large Language Models (LLMs) understand context-dependent expressions from a pragmatic standpoint, specifically in Korean.
We use both Multiple-Choice Questions (MCQs) for automatic evaluation and Open-Ended Questions (OEQs) assessed by human experts.
arXiv Detail & Related papers (2024-03-19T12:21:20Z) - No Language is an Island: Unifying Chinese and English in Financial Large Language Models, Instruction Data, and Benchmarks [75.29561463156635]
ICE-PIXIU uniquely integrates a spectrum of Chinese tasks, alongside translated and original English datasets.
It provides unrestricted access to diverse model variants, a compilation of diverse cross-lingual and multi-modal instruction data, and an evaluation benchmark with expert annotations.
arXiv Detail & Related papers (2024-03-10T16:22:20Z) - OMGEval: An Open Multilingual Generative Evaluation Benchmark for Large
Language Models [59.54423478596468]
We introduce OMGEval, the first Open-source Multilingual Generative test set that can assess the capability of LLMs in different languages.
For each language, OMGEval provides 804 open-ended questions, covering a wide range of important capabilities of LLMs.
Specifically, the current version of OMGEval includes 5 languages (i.e., Zh, Ru, Fr, Es, Ar)
arXiv Detail & Related papers (2024-02-21T04:42:41Z) - LLaMA Beyond English: An Empirical Study on Language Capability Transfer [49.298360366468934]
We focus on how to effectively transfer the capabilities of language generation and following instructions to a non-English language.
We analyze the impact of key factors such as vocabulary extension, further pretraining, and instruction tuning on transfer.
We employ four widely used standardized testing benchmarks: C-Eval, MMLU, AGI-Eval, and GAOKAO-Bench.
arXiv Detail & Related papers (2024-01-02T06:29:02Z) - Advancing the Evaluation of Traditional Chinese Language Models: Towards
a Comprehensive Benchmark Suite [17.764840326809797]
We propose a novel set of benchmarks that leverage existing English datasets and are tailored to evaluate language models in Traditional Chinese.
These benchmarks encompass a wide range of tasks, including contextual question-answering, summarization, classification, and table understanding.
In this paper, we evaluate the performance of GPT-3.5, Taiwan-LLaMa-v1.0, and Model 7-C, our proprietary model, on these benchmarks.
arXiv Detail & Related papers (2023-09-15T14:52:23Z) - CMMLU: Measuring massive multitask language understanding in Chinese [133.70911295934746]
This paper introduces a comprehensive Chinese benchmark that covers various subjects, including natural science, social sciences, engineering, and humanities.
CMMLU fills the gap in evaluating the knowledge and reasoning capabilities of large language models within the Chinese context.
arXiv Detail & Related papers (2023-06-15T15:49:51Z) - XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating
Cross-lingual Generalization [128.37244072182506]
Cross-lingual TRansfer Evaluation of Multilinguals XTREME is a benchmark for evaluating the cross-lingual generalization capabilities of multilingual representations across 40 languages and 9 tasks.
We demonstrate that while models tested on English reach human performance on many tasks, there is still a sizable gap in the performance of cross-lingually transferred models.
arXiv Detail & Related papers (2020-03-24T19:09:37Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.