AC-EVAL: Evaluating Ancient Chinese Language Understanding in Large Language Models

แนวคิดหลัก

AC-EVAL aims to assess the proficiency of Large Language Models in understanding ancient Chinese through a comprehensive benchmark, highlighting areas for improvement and development.

บทคัดย่อ

AC-EVAL introduces a benchmark to evaluate Large Language Models' understanding of ancient Chinese, focusing on historical knowledge and language comprehension. The evaluation reveals potential for improvement in LLMs, especially in long text comprehension. The study compares model performance in zero-shot and few-shot scenarios, highlighting challenges faced by smaller models in processing complex information.

สถิติ

AC-EVAL comprises 3,245 multiple-choice questions across three difficulty levels.
ERNIE-Bot 4.0 and GLM-4 performed best with accuracies over 70%.
GPT models outperformed LLaMA-70B in handling extensive Chinese content.
Yi-series models showed remarkable parameter efficiency but struggled with few-shot learning.
Few-shot COT underperformed compared to zero-shot settings across all parameter sizes.

คำพูด

"By highlighting the strengths and weaknesses of LLMs, AC-EVAL aims to promote their development and application forward." - Yuting Wei et al.
"Our analysis shows that Chinese LLMs outperform English ones in ancient Chinese." - Yuting Wei et al.
"The broad range of knowledge required in our tasks reveals that LLMs encounter difficulties in grasping underlying rules." - Yuting Wei et al.

ข้อมูลเชิงลึกที่สำคัญจาก

AC-EVAL

by Yuting Wei,Y... ที่ arxiv.org 03-12-2024

https://arxiv.org/pdf/2403.06574.pdf

สอบถามเพิ่มเติม

How can human evaluation be incorporated into the assessment of LLMs for a more qualitative analysis?

Human evaluation can be integrated into the assessment of Large Language Models (LLMs) by involving experts or scholars in ancient Chinese literature to provide subjective insights and feedback on the model's performance. These human evaluators can analyze the nuances, cultural references, and contextual understanding displayed by the LLMs in their responses. By comparing the model-generated outputs with expert evaluations, researchers can gain a deeper understanding of how well LLMs capture the essence and subtleties of ancient Chinese language and culture.

What are the implications of using a multiple-choice format for assessing generative capabilities of LLMs?

Using a multiple-choice format to assess generative capabilities in Large Language Models (LLMs) may not fully capture their true potential in generating coherent and contextually relevant responses. Generative tasks require models to produce output from scratch rather than selecting from predefined options. The multiple-choice format limits creativity, originality, and flexibility in response generation, potentially overlooking unique insights that an open-ended approach would reveal. Therefore, relying solely on multiple-choice questions may underestimate an LLM's actual generative abilities.

How can the AC-EVAL benchmark be expanded to include open-ended tasks for a more comprehensive evaluation?

To enhance the AC-EVAL benchmark with open-ended tasks for a more comprehensive evaluation:

Introduce prompts that require free-form text responses instead of predefined choices.
Develop scoring rubrics or criteria based on linguistic accuracy, coherence, relevance to context, and depth of understanding.
Include tasks like essay writing on specific historical events or literary analyses that demand critical thinking skills.
Incorporate creative challenges such as poetry composition or narrative storytelling to assess expressive language capabilities.
Engage domain experts to evaluate these open-ended responses qualitatively for nuanced insights beyond quantitative metrics.
Provide opportunities for models to showcase their ability to generate diverse content without constraints imposed by fixed answer options.

By integrating open-ended tasks into AC-EVAL, researchers can obtain a holistic view of LLMs' proficiency in ancient Chinese comprehension beyond simple selection-based assessments.