#LLMs are becoming increasingly powerful, and it is important to have a way to evaluate their performance. An #LLM evaluation benchmark is a set of tasks that are used to measure the performance of an LLM. The results of these tasks can be used to compare different LLMs and to track their progress over time. There are many different LLM evaluations benchmark available, each with its own strengths and weaknesses. I'll discuss some of the most popular LLM evaluation benchmarks and explain them in simple terms. #MMLU MMLU (Massive Multitask Language Understanding) is a benchmark that measures the performance of an LLM on a variety of language understanding tasks. These tasks include question-answering, natural language inference, and summarization. MMLU is a good benchmark for evaluating the general language understanding ability of an LLM. #NarrativeQA NarrativeQA is a benchmark that measures the performance of an LLM on the task of answering questions about a story. The stories in NarrativeQA are typically long and complex, and the questions require the LLM to have a good understanding of the story. NarrativeQA is a good benchmark for evaluating the ability of an LLM to understand long and complex text. #GSM8k GSM8k (Generating Stories from Multiple Sources) is a benchmark that measures the performance of an LLM on the task of generating a story based on a set of facts. The facts in GSM8k are typically short and simple, and the LLM is expected to generate a story that is consistent with the facts. GSM8k is a good benchmark for evaluating the ability of an LLM to generate text that is consistent with a given set of facts. #BLEU-4 WMT 2014 - BLEU-4 (Workshop on Machine Translation 2014 - Bilingual Evaluation Understudy - 4) is a benchmark that measures the performance of an LLM on the task of machine translation. The BLEU-4 score is a measure of the similarity between the LLM's translation and a human translation. WMT 2014 - BLEU-4 is a good benchmark for evaluating the ability of an LLM to translate text from one language to another. #TruthfulQA TruthfulQA is a benchmark that measures the performance of an LLM on the task of answering questions about the truth of a statement. The questions in TruthfulQA are typically about the factual accuracy of a statement, and the LLM is expected to answer the questions correctly. TruthfulQA is a good benchmark for evaluating the ability of an LLM to determine the truth of a statement. #HellaSwag HellaSwag is a benchmark that measures the performance of an LLM on the task of answering questions about commonsense knowledge. The questions in HellaSwag are typically about everyday situations, and the LLM is expected to answer the questions correctly. So, while benchmarks might sound technical, they're essentially thorough and methodical report cards for our AI. These guideposts help us nurture AI that's not just intelligent but also trustworthy and relatable!
Great write up Waseem! I’d be curious to hear what do you think of products that are entering the market to evaluate LLM outputs and hallucinations?
There is also a new benchmark for logic reasoning error detection in Chain of Thought prompted answers. TL;DR LLMs are not good at finding them, but do much better fixing them once they are prompted to correct one. https://github.com/WHGTyen/BIG-Bench-Mistake
Super helpful explainer. Thank you!
Excellent summary and information Waseem!. As an risk management and audit professional and I am working on addressing auditing the entire life cycle. This is very helpful and I will research further to understand how the benchmark works. Thank you!
Health and generative AI CTO. Investor. Director.
5moWaseem, do you know of anyone working on a benchmark related to understanding and working with data in-prompt, as opposed to text-to-SQL/code or the like?