LLM Output — Evaluating, debugging, and interpreting

Author(s): Lan Chu

Originally published on Towards AI.

LLMs are not useful if they are not sufficiently accurate. In this article, we will be looking at a few methods to evaluate, debug, and interpret the performance of the LLMs, particularly the model I will be using in this article, which is GPT-3.5-Turbo. You can apply the techniques in this article to other LLMs.

Photo by Jeremy Bishop on Unsplash.

Accuracy is an umbrella term that refers to various metrics tailored to specific tasks, such as exact-match in text classification, F1 score in question answering, NDCG in information retrieval, and ROUGE in summarization (see HELM, Holistic Evaluation of Language Models). This article will concentrate on assessing accuracy in sentiment analysis and text summarization.

Obviously, the most reliable way to evaluate an LLM system is to create an evaluation dataset and compare the model-generated output with the evaluation set. The downside of this approach is that it is money and time-consuming to make one. So, when we don’t have such an evaluation set, what should we do?

Sentiment analysis is an iconic task in NLP that has been widely used across various sectors related to customer reviews about products and services. In this article, we will do a sentiment analysis on how concerned the… Read the full blog for free on Medium.

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Published via Towards AI