With the GenAI era upon us, the use of large language models (LLMs) has grown exponentially. However, as with any technology in its hype cycle, GenAI practitioners run the risk of neglecting to verify the trust and accuracy of an LLM’s outputs in favor of its quick implementation and use. Therefore, developing checks and balances for the safe and socially responsible evaluation and use of LLMs is not only best business practice but critical to fully understand their accuracy and performance.
Regular evaluation of large language models helps developers identify their strengths and weaknesses and enables them to detect and mitigate risks including misleading or inaccurate code they may generate. However, not all LLMs are created equal, so evaluating their output, nuances, and complexities with consistent results can be a challenge. We examine some considerations to keep in mind when judging the effectiveness and performance of large language models.
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The complexity of large language model evaluation
Fine-tuning a large language model for your use case can feel like training a talented but enigmatic new colleague. LLMs excel at generating ample amounts of code quickly, but your mileage on the quality of that code may vary.
Singular metrics such as accuracy of an LLM’s output only provide a partial indicator of performance and efficiency. For example, an LLM could produce technically flawless code, but its application within a legacy system may not perform as expected. Developers must assess the model’s grasp of the specific domain, its ability to follow instructions, and how well the LLM avoids generating biased or nonsensical content.
Crafting the right evaluation methods for your specific LLM is a complex endeavor. Standardizing tests and incorporating human-in-the-loop assessments are essential and baseline strategies. Techniques including prompt libraries and establishing fairness benchmarks can also help developers pinpoint a LLM’s strengths and weaknesses. By carefully selecting and devising a multi-level method of evaluation, developers can unlock the true power of LLMs to build robust and reliable applications.
Can large language models check themselves?
A newer method of evaluating LLMs is to incorporate a second LLM as a judge. Leveraging the sophisticated capabilities of external LLMs to fine tune another model can allow developers to quickly understand and critique code, observe output patterns, and compare responses.
LLMs can improve the quality of responses of other LLMs in the evaluation process, as multiple outputs from the same prompt can be compared and then the best or most applicable output can be selected.
Humans in the loop
Using LLMs to evaluate other LLMs doesn’t come without risks, as any model is only as good as the data it is trained on. As the adage goes, garbage in is garbage out. Therefore, it is crucial to always build a human review step into your LLM evaluation process. Human raters can provide oversight of the quality and relevance of LLM-generated content to your specific use case, ensuring it meets desired standards and is up to date. Additionally, human feedback on retrieval augmented generation (RAG) outputs can also assist in evaluating an AI’s ability to contextualize information.
However, human evaluation is not without its limitations. Humans bring their own biases and inconsistencies to the table. Both human and AI points of review and feedback is ideal, informing how large language models can iterate and improve.
LLMs and humans are better together
With LLMs becoming increasingly ubiquitous, developers can be at risk of using them without specifying if they’re well-suited to the use case. If they are the best option, determining trade-offs between various LLMs in terms of cost, latency, and performance is key, or even looking into utilizing a smaller, more targeted large language model. High-performing, general models can quickly become expensive, so it’s crucial to assess whether the benefits justify the costs.
Human evaluation and expertise are necessary in understanding and monitoring a LLM’s output, especially during the initial stages to ensure its performance aligns with real-world requirements. However, a future with successful and socially responsible AI involves a collaborative approach, leveraging human ingenuity alongside machine learning capabilities. Uniting the power of the developer community and its collective knowledge with the technology efficiency of AI is the key to making this ambition a reality.
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