ConceptPsy:A Benchmark Suite with Conceptual Comprehensiveness in Psychology
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866910488135204864 |
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| author | Zhang, Junlei He, Hongliang Song, Nirui Zhou, Zhanchao He, Shuyuan Zhang, Shuai Qiu, Huachuan Li, Anqi Dai, Yong Ma, Lizhi Lan, Zhenzhong |
| author_facet | Zhang, Junlei He, Hongliang Song, Nirui Zhou, Zhanchao He, Shuyuan Zhang, Shuai Qiu, Huachuan Li, Anqi Dai, Yong Ma, Lizhi Lan, Zhenzhong |
| contents | The critical field of psychology necessitates a comprehensive benchmark to enhance the evaluation and development of domain-specific Large Language Models (LLMs). Existing MMLU-type benchmarks, such as C-EVAL and CMMLU, include psychology-related subjects, but their limited number of questions and lack of systematic concept sampling strategies mean they cannot cover the concepts required in psychology. Consequently, despite their broad subject coverage, these benchmarks lack the necessary depth in the psychology domain, making them inadequate as psychology-specific evaluation suite. To address this issue, this paper presents ConceptPsy, designed to evaluate Chinese complex reasoning and knowledge abilities in psychology. ConceptPsy includes 12 core subjects and 1383 manually collected concepts. Specifically, we prompt GPT-4 to generate questions for each concept using carefully designed diverse prompts and hire professional psychologists to review these questions. To help to understand the fine-grained performances and enhance the weaknesses, we annotate each question with a chapter label and provide chapter-wise accuracy. Based on ConceptPsy, we evaluate a broad range of LLMs. We observe that, although some LLMs achieve similar accuracies on overall performances, they exhibit significant performance variations across different psychology concepts, even when they are models from the same series. We hope our work can facilitate the development of LLMs in the field of psychology. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_09861 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | ConceptPsy:A Benchmark Suite with Conceptual Comprehensiveness in Psychology Zhang, Junlei He, Hongliang Song, Nirui Zhou, Zhanchao He, Shuyuan Zhang, Shuai Qiu, Huachuan Li, Anqi Dai, Yong Ma, Lizhi Lan, Zhenzhong Computation and Language Artificial Intelligence The critical field of psychology necessitates a comprehensive benchmark to enhance the evaluation and development of domain-specific Large Language Models (LLMs). Existing MMLU-type benchmarks, such as C-EVAL and CMMLU, include psychology-related subjects, but their limited number of questions and lack of systematic concept sampling strategies mean they cannot cover the concepts required in psychology. Consequently, despite their broad subject coverage, these benchmarks lack the necessary depth in the psychology domain, making them inadequate as psychology-specific evaluation suite. To address this issue, this paper presents ConceptPsy, designed to evaluate Chinese complex reasoning and knowledge abilities in psychology. ConceptPsy includes 12 core subjects and 1383 manually collected concepts. Specifically, we prompt GPT-4 to generate questions for each concept using carefully designed diverse prompts and hire professional psychologists to review these questions. To help to understand the fine-grained performances and enhance the weaknesses, we annotate each question with a chapter label and provide chapter-wise accuracy. Based on ConceptPsy, we evaluate a broad range of LLMs. We observe that, although some LLMs achieve similar accuracies on overall performances, they exhibit significant performance variations across different psychology concepts, even when they are models from the same series. We hope our work can facilitate the development of LLMs in the field of psychology. |
| title | ConceptPsy:A Benchmark Suite with Conceptual Comprehensiveness in Psychology |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2311.09861 |