TurtleBench: Evaluating Top Language Models via Real-World Yes/No Puzzles (2410.05262v1)

Abstract: As the application of LLMs expands, the demand for reliable evaluations increases. Existing LLM evaluation benchmarks primarily rely on static datasets, making it challenging to assess model performance in dynamic interactions with users. Moreover, these benchmarks often depend on specific background knowledge, complicating the measurement of a model's logical reasoning capabilities. Other dynamic evaluation methods based on strong models or manual efforts may introduce biases and incur high costs and time demands, hindering large-scale application. To address these issues, we propose TurtleBench. TurtleBench collects real user guesses from our online Turtle Soup Puzzle platform that we developed. This approach allows for the relatively dynamic generation of evaluation datasets, mitigating the risk of model cheating while aligning assessments more closely with genuine user needs for reasoning capabilities, thus enhancing the reliability of evaluations. TurtleBench includes 1,532 user guesses along with the correctness of guesses after annotation. Using this dataset, we thoroughly evaluated nine of the most advanced LLMs available today. Notably, the OpenAI o1 series models did not achieve leading results in these evaluations. We propose several hypotheses for further research, such as "the latent reasoning of o1 utilizes trivial Chain-of-Thought (CoT) techniques" and "increasing CoT length not only provides reasoning benefits but also incurs noise costs."

• The paper introduces a novel benchmark that uses dynamic, user-generated yes/no puzzles to rigorously evaluate LLM reasoning.
• It employs an interactive methodology that minimizes static dataset biases, revealing notable underperformance of models like the OpenAI o1 series.
• Results indicate that larger parameter counts do not guarantee superior reasoning, highlighting the need for improved Chain-of-Thought strategies.

An Analysis of TurtleBench: Evaluating LLMs with Yes/No Puzzles

The paper "TurtleBench: Evaluating Top LLMs via Real-World Yes/No Puzzles" presents a novel benchmark designed to assess the reasoning abilities of LLMs using dynamic user-generated datasets derived from yes/no puzzles. This approach seeks to overcome limitations inherent in existing evaluation methods, such as static datasets and the introduction of biases through manual evaluations.

Introduction

As LLMs increasingly integrate into various fields, from e-commerce to healthcare, reliable evaluation mechanisms are critical. Traditional benchmarks largely depend on static datasets and background knowledge, which do not necessarily reflect real-world interaction dynamics. Additionally, static datasets can suffer from contamination, compromising evaluation integrity. TurtleBench addresses these concerns by employing real user interaction data from a specifically designed Turtle Soup Puzzle platform.

Methodology

TurtleBench harnesses the interactive nature of the Turtle Soup Puzzles to dynamically generate diverse evaluation datasets. This approach minimizes the risk of models leveraging memorized responses, thus ensuring evaluations are more aligned with genuine reasoning capabilities. The dataset includes 1,532 annotated user guesses evaluated against nine state-of-the-art LLMs. Noteworthy is that models like OpenAI's o1 series did not lead in performance, which provides significant insight into the reasoning capabilities of various architectures. Hypotheses are proposed for further investigation, such as challenges faced by the o1 models related to their Chain-of-Thought (CoT) processes.

Results

The experimental results detail significant performance disparities among models, with OpenAI's novel o1 series underperforming compared to Claude-3.5-Sonnet and GPT-4o, which achieved top accuracy above 87%. Such findings underscore the potential shortcomings in the CoT implementations within the o1 models, hinting at room for optimization in reasoning structures and alignment with user logic. Additionally, the results suggest that parameter quantity does not directly correlate with reasoning performance, as showcased by Qwen-2-72B outperforming larger parameter models such as Deepseek-V2.5.

Implications

TurtleBench's design holds several implications for both practical applications and theoretical explorations within AI. By removing the need for background knowledge and focusing purely on reasoning derived from the given context, TurtleBench presents a fairer platform for evaluating LLMs. Further, it addresses real-world applicability by providing an evolving dataset that mirrors actual user interactions, thereby positioning itself as a reliable and efficient benchmark for future model assessments.

Future Directions

The paper advocates for enhancing LLM evaluation through real-world applicability focus and dynamic dataset updates. This approach sets a foundation for future research in improving model reasoning capabilities, reducing noise in CoT reasoning processes, and eventually strengthening the robustness of AI applications. Future work could further explore improving reasoning consistency and integrating non-linear CoT strategies to address current performance variances among models.

Conclusion

TurtleBench offers a refreshing perspective on LLM evaluation by introducing dynamic, user-interaction-based datasets and focusing solely on reasoning capabilities. As such, it contributes significantly to developing more reliable, real-world applicable assessment benchmarks and provides a pathway for enhancing the reasoning capabilities of LLMs in future AI research endeavors.