Preeti
The GenAI era has seen a surge in the use of large language models (LLMs), but there is a need to ensure the trust and accuracy of their outputs. Regular evaluation of LLMs is crucial to identify strengths, weaknesses, and risks such as misleading or inaccurate code. Evaluating LLMs can be challenging due to their nuances and complexities, requiring developers to consider various factors to judge their effectiveness and performance.
Ellen Brandenberger, Senior Director of Product Innovation at Stack Overflow, emphasizes the complexity of evaluating large language models. While LLMs can generate code quickly, the quality of the code may vary. Singular metrics like accuracy are not enough to gauge performance, as factors like domain understanding, bias avoidance, and following instructions are also important.
Developing evaluation methods tailored to specific LLMs is essential, including standardizing tests and incorporating human-in-the-loop assessments. Techniques like prompt libraries and fairness benchmarks can help developers pinpoint strengths and weaknesses. Incorporating a second LLM as a judge in the evaluation process can improve the quality of responses and help developers understand and critique code.
Human evaluation is crucial in ensuring LLM-generated content meets desired standards and is relevant to specific use cases. While human biases and inconsistencies exist, a collaborative approach between humans and AI is key to successful and socially responsible AI. Balancing the benefits and costs of using LLMs, and leveraging human expertise alongside machine learning capabilities, is essential for building robust and reliable applications in the GenAI era.
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Source link: https://www.techradar.com/pro/large-language-model-evaluation-the-better-together-approach
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