INSTRUCTEVAL: Towards Holistic Evaluation of Instruction-Tuned Large Language Models

• URL: http://arxiv.org/abs/2306.04757v3
• Date: Thu, 15 Jun 2023 05:08:56 GMT
• Title: INSTRUCTEVAL: Towards Holistic Evaluation of Instruction-Tuned Large Language Models
• Authors: Yew Ken Chia, Pengfei Hong, Lidong Bing, Soujanya Poria
• Abstract summary: INSTRUCTEVAL is a more comprehensive evaluation suite designed specifically for instruction-tuned large language models. We take a holistic approach to analyze various factors affecting model performance, including the pretraining foundation, instruction-tuning data, and training methods. Our findings reveal that the quality of instruction data is the most crucial factor in scaling model performance.
• Score: 39.46610170563634
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Instruction-tuned large language models have revolutionized natural language processing and have shown great potential in applications such as conversational agents. These models, such as GPT-4, can not only master language but also solve complex tasks in areas like mathematics, coding, medicine, and law. Despite their impressive capabilities, there is still a lack of comprehensive understanding regarding their full potential, primarily due to the black-box nature of many models and the absence of holistic evaluation studies. To address these challenges, we present INSTRUCTEVAL, a more comprehensive evaluation suite designed specifically for instruction-tuned large language models. Unlike previous works, our evaluation involves a rigorous assessment of models based on problem-solving, writing ability, and alignment to human values. We take a holistic approach to analyze various factors affecting model performance, including the pretraining foundation, instruction-tuning data, and training methods. Our findings reveal that the quality of instruction data is the most crucial factor in scaling model performance. While open-source models demonstrate impressive writing abilities, there is substantial room for improvement in problem-solving and alignment. We are encouraged by the rapid development of models by the open-source community, but we also highlight the need for rigorous evaluation to support claims made about these models. Through INSTRUCTEVAL, we aim to foster a deeper understanding of instruction-tuned models and advancements in their capabilities. INSTRUCTEVAL is publicly available at https://github.com/declare-lab/instruct-eval.

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