Beware the Pitfalls: Evaluating Large Language Models with Other LLMs

 

Why does it matter?

Evaluating the capabilities and limitations of LLMs is crucial as they become more widely adopted, especially in high-stakes domains like healthcare and law.

Evaluating LLMs with LLMs offers a convenient and scalable approach to evaluating LLMs.

However, using other LLMs to evaluate LLMs can lead to systematic biases and unreliable results. Understanding the potential pitfalls of this approach is important to ensure accurate and responsible assessment of these powerful AI systems.

The Pitfalls

The key pitfalls of using LLMs to evaluate other LLMs include:

1. Biases in LLM-generated test items: LLMs may generate test items that resemble their training data, leading to inflated performance scores that do not reflect true understanding.1 The systematic assessment of such biases remains an open challenge.
2. Interdependence of generation and evaluation: The ability of an LLM to generate the correct answer and the ability to determine if an answer is correct may not be independent, potentially leading to misleading evaluations.1
3. Lack of transparency and control with closed LLMs: It is difficult to verify the training data, fine-tuning, and model changes of closed-source LLMs, making it challenging to conduct rigorous evaluations.1
4. Inconsistency of LLM-based evaluations: LLM-based evaluation metrics like G-Eval can be inconsistent, undermining the reliability of the scores.2
5. Potential for amplifying existing biases: LLM-based evaluations may amplify biases present in the training data, leading to unfair and inaccurate assessments.4

In summary, while using LLMs for evaluation may seem convenient, it requires extreme caution and extensive validation to avoid systematic biases and unreliable results. Researchers should consider alternative evaluation approaches, such as using diverse human-curated test sets, to ensure the accurate and responsible assessment of LLM capabilities.

The Mitigations

To mitigate these issues, researchers should consider the following strategies:

1. Leveraging Diverse Human-Curated Test Sets:

• Use test sets curated by domain experts that cover a wide range of topics and difficulty levels, rather than relying solely on LLM-generated test items.*
• Employ techniques like adversarial testing to identify and address biases in the test sets.

2. Incorporating Transparency and Interpretability:

• Develop open-source LLMs with transparent training processes and model architectures to enable rigorous evaluation and validation.*
• Utilize interpretability techniques, such as feature importance analysis and saliency maps, to understand the inner workings of LLMs and identify potential sources of bias.*

3. Continuous Evaluation and Monitoring:

• Implement continuous evaluation frameworks that monitor LLM performance over time and across diverse datasets, detecting deviations and anomalies.*
• Leverage tools like FiddlerAI that provide comprehensive LLM evaluation capabilities, including bias detection and performance optimization*

4. Addressing Interdependence of Generation and Evaluation:

• Explore evaluation approaches that decouple the generation and evaluation components, such as using separate models or human evaluators for these tasks.*
• Develop evaluation metrics that account for the interdependence between generation and evaluation, ensuring more reliable and unbiased assessments.*

5. Incorporating Domain Knowledge and Human Oversight:

• Involve domain experts in the evaluation process to provide context-specific insights and ensure the relevance and accuracy of the assessments.*
• Implement human-in-the-loop evaluation frameworks that leverage the strengths of both LLMs and human experts.*

Sources and Citations

1 Hagendorff, T. (2023). Running cognitive evaluations on large language models: The do’s and don’ts. arXiv preprint arXiv:2312.01276.

2 Confident AI. (2024). LLM Evaluation Metrics: Everything You Need for LLM Evaluation. https://www.confident-ai.com/blog/llm-evaluation-metrics-everything-you-need-for-llm-evaluation

[3] Intro to AI Series: Evaluating LLMs and Potential Pitfalls — YouTube. (2024). https://www.youtube.com/watch?v=e0IqkNkq1qE

[4] Madireddy, S., Lusch, B., & Ngom, M. (2023). Large language models in medicine: the potentials and pitfalls. arXiv preprint arXiv:2309.00087.