Evaluating Large Language Models Using Multiple Instruction Prompts: A Call for Robust and Meaningful Assessment

Główne pojęcia

Single-prompt evaluation of large language models leads to unstable and unreliable results. A multi-prompt evaluation approach is necessary to provide a more robust and meaningful assessment of model capabilities.

Streszczenie

The authors argue that the common practice of evaluating large language models (LLMs) using a single instruction template per task is problematic, as it leads to unstable and unreliable results. They create a large-scale dataset of over 175 paraphrased instructions for 39 tasks across three benchmarks (LMENTRY, BIG-bench Lite, and BIG-bench Hard), involving 20 different LLMs.

The analysis reveals that different instruction templates can lead to vastly different absolute and relative model performance. For example, the ranking of models can change significantly depending on the chosen prompt. The authors also find that models can exhibit drastic performance differences even for minor changes in the wording of the instruction.

To address these limitations, the authors propose a set of multi-prompt evaluation metrics tailored for different use cases, such as measuring model robustness (average performance) or suitability for a specific downstream application (maximum performance). Evaluating the models using these metrics provides new insights into their strengths and limitations, which are not captured by the original single-prompt evaluations.

The authors also show that their automatic paraphrasing method is effective, and there is no need to manually verify the paraphrases, as the metrics are largely unaffected by the quality of the paraphrases.

Finally, the authors perform a small-scale evaluation of OpenAI models, demonstrating that they are also sensitive to prompt paraphrasing, further highlighting the need for a multi-prompt evaluation approach.

Statystyki

"Different instruction templates can lead to vastly different absolute and relative model performance."
"Models can exhibit drastic performance differences even for minor changes in the wording of the instruction."
"The ranking of models can change significantly depending on the chosen prompt."

Cytaty

"We find that different instruction templates lead to very different performance, both absolute and relative."
"Instead, we propose a set of diverse metrics on multiple instruction paraphrases, specifically tailored for different use cases (e.g., LLM vs. downstream development), ensuring a more reliable and meaningful assessment of LLM capabilities."
"Our results suggest that future work should use multi-prompt LLM evaluations and choose a metric for aggregating the results according to the extrinsic needs of the evaluators."

Kluczowe wnioski z

State of What Art? A Call for Multi-Prompt LLM Evaluation

by Moran Mizrah... o arxiv.org 05-07-2024

https://arxiv.org/pdf/2401.00595.pdf

Głębsze pytania

How can the multi-prompt evaluation approach be extended to incorporate few-shot learning and in-context examples?

Incorporating few-shot learning and in-context examples into the multi-prompt evaluation approach can enhance the robustness and applicability of the evaluation process for LLMs. One way to extend this approach is to include a diverse set of in-context examples along with the instruction templates. These examples can provide additional context and information for the model to generalize better to unseen tasks. By evaluating the model's performance on a combination of instruction templates and in-context examples, we can assess its ability to adapt and perform well in various scenarios with limited training data.
Furthermore, incorporating few-shot learning involves evaluating the model's performance on tasks with a small number of examples or instances. This can be achieved by providing the model with a limited amount of training data and evaluating its performance on a set of instruction templates and in-context examples. By analyzing how well the model generalizes and performs on tasks with minimal training data, we can gain insights into its few-shot learning capabilities and adaptability.
Overall, extending the multi-prompt evaluation approach to incorporate few-shot learning and in-context examples can provide a more comprehensive assessment of an LLM's performance across different scenarios and levels of data availability.

How can the potential biases and limitations introduced by the automatic paraphrasing methods used in this study be further improved?

The automatic paraphrasing methods used in the study may introduce biases and limitations that can impact the quality and reliability of the evaluation results. To address these issues and improve the paraphrasing process, several strategies can be implemented:

Diverse Paraphrasing Techniques: Incorporate a variety of paraphrasing techniques to ensure a broader range of paraphrases that capture different nuances and variations in the instruction templates. This can help reduce bias introduced by a single paraphrasing method.

Human-in-the-Loop Verification: Implement a human-in-the-loop verification process to manually review and validate the automatically generated paraphrases. Human annotators can ensure the accuracy, relevance, and coherence of the paraphrases, reducing the risk of introducing biases or errors.

Bias Detection and Mitigation: Use bias detection algorithms to identify and mitigate any potential biases in the paraphrased instructions. By analyzing the paraphrases for biased language, stereotypes, or inaccuracies, the quality and fairness of the paraphrasing process can be improved.

Regular Updates and Maintenance: Continuously update and refine the paraphrasing models based on feedback and evaluation results. Regular maintenance and improvement of the paraphrasing methods can help enhance their effectiveness and reduce biases over time.

By implementing these strategies, the potential biases and limitations introduced by automatic paraphrasing methods can be mitigated, leading to more accurate and reliable evaluation results.

How can the insights from this study be applied to the development and deployment of LLMs in real-world applications, beyond just the evaluation process?

The insights from this study can have significant implications for the development and deployment of LLMs in real-world applications, going beyond just the evaluation process. Here are some ways these insights can be applied:

Prompt Design and Optimization: Developers can use the findings to optimize prompt design for LLMs, ensuring that instruction templates are carefully crafted to maximize model performance. By considering the sensitivity of LLMs to prompt variations, developers can create more effective prompts for specific tasks and applications.

Model Selection and Tuning: When selecting and fine-tuning LLMs for specific tasks, developers can leverage the multi-prompt evaluation approach to choose models that exhibit both high performance and robustness across different instruction templates. This can lead to better model selection and tuning decisions for real-world applications.

Bias Detection and Mitigation: The study highlights the importance of detecting and addressing biases introduced by prompt paraphrasing. Developers can apply similar bias detection techniques to identify and mitigate biases in LLMs, ensuring fair and unbiased performance in real-world applications.

Adaptation to Varied Use Cases: By understanding the impact of prompt paraphrasing on LLM performance, developers can tailor models to specific use cases and scenarios. This customization can enhance the applicability and effectiveness of LLMs in diverse real-world applications.

Overall, the insights from this study can inform the development, optimization, and deployment of LLMs in real-world applications, leading to more reliable and effective natural language processing solutions.