DyVal: Dynamic Evaluation of Large Language Models for Reasoning Tasks (2309.17167v3)

Abstract: LLMs have achieved remarkable performance in various evaluation benchmarks. However, concerns are raised about potential data contamination in their considerable volume of training corpus. Moreover, the static nature and fixed complexity of current benchmarks may inadequately gauge the advancing capabilities of LLMs. In this paper, we introduce DyVal, a general and flexible protocol for dynamic evaluation of LLMs. Based on our framework, we build graph-informed DyVal by leveraging the structural advantage of directed acyclic graphs to dynamically generate evaluation samples with controllable complexities. DyVal generates challenging evaluation sets on reasoning tasks including mathematics, logical reasoning, and algorithm problems. We evaluate various LLMs ranging from Flan-T5-large to GPT-3.5-Turbo and GPT-4. Experiments show that LLMs perform worse in DyVal-generated evaluation samples with different complexities, highlighting the significance of dynamic evaluation. We also analyze the failure cases and results of different prompting methods. Moreover, DyVal-generated samples are not only evaluation sets, but also helpful data for fine-tuning to improve the performance of LLMs on existing benchmarks. We hope that DyVal can shed light on future evaluation research of LLMs. Code is available at: https://github.com/microsoft/promptbench.

Dynamic Evaluation of LLMs: An Analysis of DyVal

DyVal offers a nuanced framework for the dynamic evaluation of LLMs, addressing notable limitations of current evaluation methodologies. While static benchmarks have dominated the landscape, issues such as data contamination and inadequate assessment of LLMs' evolving capabilities persist. DyVal emerges as a solution, offering dynamic sample generation and complexity modulation, essential for rigorous evaluation in reasoning tasks.

Key Contributions

1. Dynamic Evaluation Protocol: DyVal introduces a protocol facilitating the dynamic generation of evaluation samples, mitigating data contamination risks. This approach allows for the continuous evolution of test datasets, ensuring they remain relevant as LLMs advance.
2. Graph-Informed Generation: By leveraging Directed Acyclic Graphs (DAGs), DyVal enables the construction of reasoning tasks with varying complexities. Tree-based DAGs, suitable for tasks with hierarchical structures such as logic and arithmetic, and General DAGs for complex, non-linear interactions, exemplify this flexibility.
3. Comprehensive Evaluation Framework: The DyVal framework supports fine-tuning of complexity through constraints like depth, width, and additional perturbations. This ensures that LLMs are evaluated across a spectrum of challenges, enhancing the robustness of the assessment.
4. Integration with Existing Benchmarks: DyVal complements existing benchmarks, co-evolving by generating new challenging scenarios and integrating techniques such as adversarial prompting and out-of-distribution robustness checks.

Experimental Insights

The experimental results across seven reasoning tasks — including mathematics, logical reasoning, and algorithmic challenges — reveal significant insights. Models like GPT-4 demonstrated robust performance, yet struggled with increasing complexity, particularly in abductive logic tasks. Interestingly, the poor performance of some LLMs on DyVal-generated samples, despite their purported success on static benchmarks, underscores potential contamination issues or overfitting phenomena.

Human evaluators, contrastingly, also found challenges in complex tasks, highlighting areas where LLM capabilities could be perceived as competitive.

Practical and Theoretical Implications

The implications of DyVal are profound:

• Practical: The dynamic nature of DyVal allows developers to fine-tune models on dynamically generated samples, improving performance on diverse benchmarks without extensive manual data collection. The fine-tuning results also underscore the potential for improving LLMs using DyVal-generated datasets, notably enhancing existing benchmark performance.
• Theoretical: By providing a framework that continuously adapts to LLM evolution, DyVal shifts the focus towards a more rigorous examination of model generalization capabilities. It stimulates future research into more sophisticated dynamic evaluation techniques and their integration into AI development pipelines.

Future Directions

Future work could expand DyVal’s application beyond reasoning tasks to broader NLP challenges, utilizing its framework to test adaptive responses in more varied linguistic contexts. Additionally, integrating more granular adversarial and robustness assessments could further refine LLM evaluations.

In conclusion, DyVal sets a new standard for dynamic evaluation, advancing both theory and practice in the assessment of LLMs. Its potential to evolve alongside AI models makes it a valuable tool for ongoing research and development in AI evaluation methodologies.