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INSTRUCTEVAL: Towards Holistic Evaluation of Instruction-Tuned Large Language Models (2306.04757v3)

Published 7 Jun 2023 in cs.CL and cs.AI
INSTRUCTEVAL: Towards Holistic Evaluation of Instruction-Tuned Large Language Models

Abstract: Instruction-tuned LLMs 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 LLMs. 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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Holistic Evaluation of Instruction-Tuned LLMs with InstructEval

The paper presents a comprehensive evaluation suite, InstructEval, designed to assess the capabilities and performance of instruction-tuned LLMs. The introduction of such an analytical framework is of critical importance, given the black-box nature and complex architectures of contemporary models like GPT-4. These models have demonstrated proficiency across various domains, including mathematics, coding, medicine, and law, yet a holistic understanding of their full potential remains elusive.

Key Features of InstructEval

The InstructEval suite aims to move beyond traditional evaluation methods by incorporating a multifaceted approach that examines:

  1. Problem-solving abilities: Utilizing benchmarks that cover arithmetic, programming, and general knowledge.
  2. Writing proficiency: Assessment of models in informational, creative, professional, and argumentative writing tasks.
  3. Alignment with human values: Focusing on helpfulness, honesty, and harmlessness to ensure ethical considerations in AI behavior.

This methodologically rigorous evaluation is predicated on various critical factors including pretraining foundations, instruction-tuning data, and training methodologies.

Insights and Findings

The findings from deploying InstructEval are noteworthy:

  • Instruction Data Quality: The quality of instruction data emerges as the primary determinant in scaling model performance. Models trained with high-quality, diverse instructions displayed superior problem-solving capabilities.
  • Open Source vs. Closed Source Models: Open-source models reveal commendable writing abilities but manifest notable deficiencies in problem-solving and ethical alignment. Despite being trained on synthetic instructions mimicking models like GPT-3, their performance gains are often limited.
  • Specialization and Scalability: The paper highlights the potential specialization of models across different tasks. For instance, proficiency in problem-solving does not necessarily translate into superior writing skills or ethical alignment.

Challenges in Model Evaluation

The task of evaluating LLMs is complicated by several factors:

  • Inscrutable Closed-Source Models: Closed-source models limit transparency and reproducibility. Their assessment is challenging due to restricted access and unknown internal configurations.
  • Fast-paced Open-Source Developments: While the open-source community rapidly develops new models, rigorous evaluations lag, leading to potentially misleading claims about model capabilities.
  • Broader Capability Scope: As models gain the ability to solve domain-specific problems and use external tools, a more nuanced and extensive evaluation is required, incorporating usage scenarios and human-centric behavior.

Future Directions

The implications of InstructEval extend beyond mere model benchmarking. It lays a foundation for the future development of LLMs across multilingual and multimodal dimensions, promoting the advancement of more versatile, ethically-aligned AI systems.

In conclusion, InstructEval fills a critical gap in the systematic evaluation of instruction-tuned LLMs, offering a detailed panorama of their abilities and shortcomings. Through such comprehensive evaluation frameworks, researchers can drive the responsible and effective advancement of AI technologies.

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Authors (4)
  1. Yew Ken Chia (24 papers)
  2. Pengfei Hong (12 papers)
  3. Lidong Bing (144 papers)
  4. Soujanya Poria (138 papers)
Citations (50)
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