Pragmatic Competence Evaluation of Large Language Models for Korean

• URL: http://arxiv.org/abs/2403.12675v1
• Date: Tue, 19 Mar 2024 12:21:20 GMT
• Title: Pragmatic Competence Evaluation of Large Language Models for Korean
• Authors: Dojun Park, Jiwoo Lee, Hyeyun Jeong, Seohyun Park, Sungeun Lee,
• Abstract summary: This study explores the pragmatic competence of Large Language Models (LLMs) in the context of Korean. We employ two distinct evaluation setups: the conventional MCQ format, adapted for automatic evaluation, and Open-Ended Questions (OEQs) Our findings reveal that GPT-4 excels, scoring 81.11 and 85.69 in the MCQ and OEQ setups, respectively, with HyperCLOVA X, an LLM optimized for Korean, closely following, demonstrating a score of 81.56 with a marginal difference of 4.13 points compared to GPT-4.
• Score: 0.6757476692230009
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The current evaluation of Large Language Models (LLMs) predominantly relies on benchmarks focusing on their embedded knowledge by testing through multiple-choice questions (MCQs), a format inherently suited for automated evaluation. Our study extends this evaluation to explore LLMs' pragmatic competence--a facet previously underexamined before the advent of sophisticated LLMs, specifically in the context of Korean. We employ two distinct evaluation setups: the conventional MCQ format, adapted for automatic evaluation, and Open-Ended Questions (OEQs), assessed by human experts, to examine LLMs' narrative response capabilities without predefined options. Our findings reveal that GPT-4 excels, scoring 81.11 and 85.69 in the MCQ and OEQ setups, respectively, with HyperCLOVA X, an LLM optimized for Korean, closely following, especially in the OEQ setup, demonstrating a score of 81.56 with a marginal difference of 4.13 points compared to GPT-4. Furthermore, while few-shot learning strategies generally enhance LLM performance, Chain-of-Thought (CoT) prompting introduces a bias toward literal interpretations, hindering accurate pragmatic inference. Considering the growing expectation for LLMs to understand and produce language that aligns with human communicative norms, our findings emphasize the importance for advancing LLMs' abilities to grasp and convey sophisticated meanings beyond mere literal interpretations.

Related papers

• MultiPragEval: Multilingual Pragmatic Evaluation of Large Language Models [0.5822010906632046]
  MultiPragEval is a robust test suite designed for the multilingual pragmatic evaluation of LLMs across English, German, Korean, and Chinese. Our findings demonstrate that Claude3-Opus significantly outperforms other models in all tested languages.
  arXiv  Detail & Related papers  (2024-06-11T21:46:03Z)
• Beyond Metrics: Evaluating LLMs' Effectiveness in Culturally Nuanced, Low-Resource Real-World Scenarios [29.56889133557681]
  This research evaluates the performance of seven leading Large Language Models (LLMs) in sentiment analysis on a dataset derived from WhatsApp chats. We find that while Mistral-7b and Mixtral-8x7b achieved high F1 scores, they and other LLMs such as GPT-3.5-Turbo, Llama-2-70b, and Gemma-7b struggled with understanding linguistic and contextual nuances. GPT-4 and GPT-4-Turbo excelled in grasping diverse linguistic inputs and managing various contextual information.
  arXiv  Detail & Related papers  (2024-06-01T07:36:59Z)
• Benchmarking Large Language Models on CFLUE -- A Chinese Financial Language Understanding Evaluation Dataset [7.954348293179786]
  We propose CFLUE, a benchmark to assess the capability of large language models (LLMs) across various dimensions. In knowledge assessment, it consists of 38K+ multiple-choice questions with associated solution explanations. In application assessment, it features 16K+ test instances across distinct groups of NLP tasks such as text classification, machine translation, relation extraction, reading comprehension, and text generation.
  arXiv  Detail & Related papers  (2024-05-17T05:03:40Z)
• 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)
• CIF-Bench: A Chinese Instruction-Following Benchmark for Evaluating the Generalizability of Large Language Models [53.9835961434552]
  We introduce the Chinese Instruction-Following Benchmark (CIF-Bench) to evaluate the generalizability of large language models (LLMs) to the Chinese language. CIF-Bench comprises 150 tasks and 15,000 input-output pairs, developed by native speakers to test complex reasoning and Chinese cultural nuances. To mitigate data contamination, we release only half of the dataset publicly, with the remainder kept private, and introduce diversified instructions to minimize score variance.
  arXiv  Detail & Related papers  (2024-02-20T16:02:12Z)
• 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)
• Are Large Language Model-based Evaluators the Solution to Scaling Up Multilingual Evaluation? [20.476500441734427]
  Large Language Models (LLMs) excel in various Natural Language Processing (NLP) tasks. Their evaluation, particularly in languages beyond the top $20$, remains inadequate due to existing benchmarks and metrics limitations.
  arXiv  Detail & Related papers  (2023-09-14T06:41:58Z)
• L-Eval: Instituting Standardized Evaluation for Long Context Language Models [91.05820785008527]
  We propose L-Eval to institute a more standardized evaluation for long context language models (LCLMs) We build a new evaluation suite containing 20 sub-tasks, 508 long documents, and over 2,000 human-labeled query-response pairs. Results show that popular n-gram matching metrics generally can not correlate well with human judgment.
  arXiv  Detail & Related papers  (2023-07-20T17:59:41Z)
• 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)
• Evaluating the Performance of Large Language Models on GAOKAO Benchmark [53.663757126289795]
  This paper introduces GAOKAO-Bench, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples. With human evaluation, we obtain the converted total score of LLMs, including GPT-4, ChatGPT and ERNIE-Bot. We also use LLMs to grade the subjective questions, and find that model scores achieve a moderate level of consistency with human scores.
  arXiv  Detail & Related papers  (2023-05-21T14:39:28Z)

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.