TELeR: New Taxonomy Framework for Standardizing LLM Prompt Benchmarking on Complex Tasks
Key Takeaways
- ▸TELeR provides the first general taxonomy for designing and categorizing prompts used in LLM benchmarking studies on complex tasks
- ▸The framework addresses performance variability caused by different prompt types, styles, and levels of detail in LLM evaluations
- ▸Standardized prompt categorization enables meaningful comparisons across different studies and more accurate assessment of LLM capabilities
Summary
Researchers have introduced TELeR, a general taxonomy designed to standardize how large language models are evaluated on complex, ill-defined tasks. The framework addresses a critical gap in LLM benchmarking by providing a structured approach to prompt design, accounting for variations in performance that result from different prompt types, styles, and levels of detail. This taxonomy enables researchers to categorize and report the specific properties of prompts used in studies, facilitating more meaningful comparisons across different benchmarking efforts.
The paper, submitted in May 2023 and revised through October 2023, fills an important need in the field. While LLMs have demonstrated strong performance in traditional conversational settings, comprehensive benchmarking studies specifically focused on complex tasks remain limited. The lack of standardization in prompt engineering has made it difficult to draw accurate conclusions about LLM capabilities and limitations. By establishing a common standard through TELeR, the research community can conduct more rigorous and comparable evaluations of how different LLMs handle complex problem-solving scenarios.
- The taxonomy addresses a significant gap in research, as comprehensive benchmarking of LLMs on ill-defined complex tasks has been largely understudied
Editorial Opinion
TELeR represents an important step toward rigorous, reproducible evaluation of LLMs on challenging real-world tasks. By establishing a standardized framework for prompt design and categorization, this taxonomy could significantly improve the quality and comparability of benchmarking studies across the field. The research highlights a critical need for methodological standardization in prompt engineering—a factor that has been largely overlooked despite its profound impact on measured LLM performance. As the field moves beyond simple conversational benchmarks toward more complex task evaluation, such taxonomic frameworks will be essential for building trustworthy and comparable assessments of AI capabilities.
