Most industries are going through rapid transformation as a result of Generative AI (Gen AI). Within these industries, almost every department is either in the works of taking advantage of Gen AI or has definite plans to implement it in the near future. Building an LLM or Gen AI model is still expensive. The companies make use of available Gen AI models. With so many established Gen AI models, how do we know how well these AI models perform? These models are evaluated and provided a score to assess the effectiveness of Gen AI outputs.
The evaluation scores will set fair expectations for the requirement. It answers important questions like how relevant, factually accurate, and unbiased the model can be for a specific requirement. For example, if a text summarization model is used to create product descriptions, the evaluation score can provide us with the following estimates:
- Accurate: Will the generated descriptions be factually correct and consistent with the product details?
- Relevant: Are the features mentioned in the description generic or relevant to the product?
- Bias: Will the language and context used be free from bias?
There is a wide range of evaluation scores and is dependent on the task the Gen AI model will be performing. Following are a couple of such evaluation methods for specific tasks:
- Text-based metrics: Text summarization models evaluate the results by comparing them to a human-written reference. A high score indicates that the tool can provide an output that contains multiple sequences of words (n-grams) against a human (expert) written reference description. BLEU (BiLingual Evaluation Understudy) and ROUGE (Recall-Oriented Understudy for Gisting Evaluation) scores are commonly considered for such tasks.
- Quality and relevance scores: Beyond simple similarities, the assessment is based on the uniqueness of the output. Human experts might rate the quality and relevance for more subjective and nuanced evaluation. An advertising agency would prefer such evaluation to assess the creativity, memorability, and brand alignment of slogans generated by the Gen AI model.
These evaluation scores are not perfect. Cultural nuances and stylistic elements might be missed. Human evaluation might get subjective too. The selection of evaluation method is crucial as a legal advocate will prefer accuracy whereas a creative content writer might prefer the assessment of imaginative elements. Also, when style is a parameter for consideration, evaluating narrative structure, and for ethical considerations, evaluating the presence of stereotypes or bias in the output should be evaluated.
In the current landscape, the gap between quantifiable metrics and qualitative metrics of output can be bridged by human evaluation. Still, evaluation scores remain essential for gauging the limitations of a Gen AI model. A lot of research and investment are being contributed towards the evolution of evaluation methods which will lead to sophisticated and impactful applications.
