How to Evaluate Large Language Models (LLMs)

Large Language Models (LLMs) like GPT, Falcon, Gemini, BERT, Dolly etc have revolutionized the field of natural language processing that have demonstrated remarkable capabilities across a broad spectrum of tasks such as Chatbots, text generation, content management etc. As a result, organizations have realized that Gen AI has great potential for digital transformation and are continuously trying to integrate, fine tune & develop the LLM Models in their AI landscape. However, it is extremely important to evaluate the LLM Models to ensure their effectiveness, precision, performance, quality & integrity. Several metrices, benchmarks, frameworks & tools have evolved to help evaluate the LLM Models. In this article we will explore these benchmarks & frameworks and also understand how exactly model evaluation works.

?LLM Models Evaluation Aspects

LLM Models are evaluated across multiple aspects, each having multiple metrices. Please see fig 1 below:

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?LLM Models Evaluation Metrices

There are several standard metrics for evaluating LLMs. Usually, LLM outputs are very difficult to evaluate.?There are numerous established methods available for calculating metric scores?—?some utilize neural networks, including embedding models and LLMs, while others are based entirely on statistical analysis. Some of the popular metrics are listed below:

1.????? Perplexity

Perplexity is a measure of how well a language model predicts a sample of text. It is calculated as the inverse probability of the test set normalized by the number of words.

2.????? Accuracy

Accuracy is a measure of how well a language model makes correct predictions. It is calculated as the number of correct predictions divided by the total number of predictions.

3.????? F1-score

F1-score is a measure of a language model's balance between precision and recall. It is calculated as the harmonic mean of precision and recall.

4.????? ROUGE score

?ROUGE score is a measure of how well a language model generates text that is similar to reference texts. It is commonly used for text generation tasks such as summarization and paraphrasing. To is calculated by comparing generated text to one or more reference texts.

?5.????? BLEU score

?BLEU score is a measure of how well a language model generates text that is fluent and coherent. It is commonly used for text generation tasks such as machine translation and image captioning.

BLEU score can be calculated by comparing the generated text to one or more reference texts and calculating a score based on the n-gram overlap between them.

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6.????? METEOR score

METEOR score is a measure of how well a language model generates text that is accurate and relevant. It combines both precision and recall evaluating the quality of the generated text. METEOR score can be calculated by comparing the generated text to one or more reference texts and calculating a score based on the harmonic mean of precision and recall.

7.????? Question Answering Metrics

Question answering metrics are used to evaluate the ability of a language model to provide correct answers to questions. Common metrics include accuracy, F1-score, and Macro F1-score.

Question answering metrics can be calculated by comparing the generated answers to one or more reference answers and calculating a score based on the overlap between them.

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8.????? Sentiment Analysis Metrics

Sentiment analysis metrics are used to evaluate the ability of a language model to classify sentiments correctly. Common metrics include accuracy, weighted accuracy, and macro F1-score.

Sentiment analysis metrics can be calculated by comparing the generated sentiment labels to one or more reference labels and calculating a score based on the overlap between them.

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9.????? Named Entity Recognition Metrics

Named entity recognition metrics are used to evaluate the ability of a language model to identify entities correctly. Common metrics include accuracy, precision, recall, and F1-score.

Named entity recognition metrics can be calculated by comparing the generated entity labels to one or more reference labels and calculating a score based on the overlap between them.

10.? Contextualized Word Embeddings

Contextualized word embeddings are used to evaluate the ability of a language model to capture context and meaning in word representations. They are generated by training the language model to predict the next word in a sentence given the previous words.

Contextualized word embeddings can be evaluated by comparing the generated embeddings to one or more reference embeddings and calculating a score based on the similarity between them.

?LLM Models Evaluation Benchmarks

An LLM benchmark is a?standardized performance test used to evaluate various capabilities of AI language models. A benchmark usually consists of a dataset, a collection of questions or tasks, and a scoring mechanism. After undergoing the benchmark’s evaluation, models are usually awarded a score from 0 to 100. Benchmark scores reveal where a model excels and when it falls short. Following is a list of popular benchmarks:

?1.????? ARC

?ARC (AI2 Reasoning Challenge) is a question-answer (QA) benchmark that’s designed to test an LLM’s knowledge and reasoning capability. ARC’s dataset consists of?7787?four-option?multiple-choice science questions?that range from a 3rd?to 9th-grade difficulty level. ARC’s questions are divided into Easy and Challenge sets that test different types of knowledge such as factual, definition, purpose, spatial, process, experimental, and algebraic.?

?2.????? HellaSwag

?HellaSwag( Harder Endings, Longer contexts, and Low-shot Activities for Situations with Adversarial Generations) benchmark tests the?commonsense reasoning?and natural language inference (NLI) capabilities of LLMs through sentence completion exercises.

?3.????? MMLU

?Massive Multitask Language Understanding (MMLU) is a broad, but important benchmark that measures an LLM’s NLU, i.e., how well it?understands?language and, subsequently, its ability to solve problems with the knowledge to which it was exposed during training.

?4.????? TruthfulQA

?While an LLM may be capable of producing coherent and well-constructed responses, it doesn’t necessarily mean they’re accurate. The TruthfulQA?benchmark attempts to address this, i.e., language models’ tendency to hallucinate, by measuring a model’s ability to generate truthful answers to questions.?

5.????? WinoGrande

Winogrande is a benchmark that evaluates an LLM’s commonsense reasoning abilities and is based on the Winograd Schema Challenge (WSC) machine learning tests. The benchmark presents a series of pronoun resolution problems: where two near-identical sentences have two possible answers, which change based on a trigger word.

6.????? GSM8K

The?GSM8K (which stands for Grade School Math 8K) benchmark measures a model’s multi-step mathematical reasoning abilities. It contains a corpus of around 8,500 grade-school-level math word problems devised by humans, which is divided into 7,500 training problems and 1,00 test problems.?

7.????? SuperGlue

The General Language Understanding Evaluation (GLUE) benchmark tests an LLM’s NLU capabilities and was notable upon its release for its variety of assessments. SuperGlue improves upon GLUE with a more diverse and challenging collection of tasks that assess a model’s performance across eight subtasks and two metrics, with their average providing an overall score.?

8.????? HumanEval

?HumanEval (also often referred to as HumanEval-Python) is a benchmark designed to measure a model’s ability to generate functionally correct code; it consists of?the HumanEval dataset?and?the pass@k metric.

This HumanEval dataset was carefully designed and contains 164 diverse coding challenges that include several unit tests (7.7 on average).?The pass@k metric calculates the probability that at least one of?k?generated code samples pass the coding challenge’s unit tests, given that there are?c?correct samples from?n?generated samples.

?9.????? MT Bench

MT Bench is a benchmark that evaluates a language model’s capability to effectively engage in multi-turn dialogues. By simulating the back-and-forth conversations that LLMs would have in real-life situations, MT-Bench provides a way to measure how effectively chatbots follow instructions and the natural flow of conversations.?

Above benchmarks are published on LLM Leaderboards so be compared against one another.? Popular Leaderboards are Hugging Face, Berkeley Function-Calling Leaderboard, CanAiCode Leaderboard, Open Multilingual LLM Evaluation Leaderboard, Massive Text Embedding Benchmark (MTEB) Leaderboard, AlpacaEval Leaderboard, Uncensored General Intelligence Leaderboard (UGI), LMSYS Chatbot Arena Leaderboard, ScaleAI Leaderboad etc.

?LLM Models Evaluation Frameworks

Numerous frameworks have been devised specifically for the evaluation of LLMs. Below, we highlight some of the most widely recognized ones.

1.????? Azure AI Studio Evaluation (Microsoft)

Azure AI Studio is an all-in-one AI platform for building, evaluating, and deploying generative AI solutions and custom copilots.Technical Landscape: No code: model catalog in AzureML studio & AI studio, Low-code: as CLI, Pro-code: as azureml-metrics SDK.

2.????? Prompt Flow

A suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, and evaluation to production, deployment, and monitoring.

3.????? Weights & Biases

A Machine Learning platform to quickly track experiments, version and iterate on datasets, evaluate model performance, reproduce models, visualize results and spot regressions, and share findings with colleagues.

4.????? LangSmith (LangChain)

Helps the user trace and evaluate language model applications and intelligent agents to help user move from prototype to production.

5.????? TruLens (TruEra)

?TruLens provides a set of tools for developing and monitoring neural nets, including LLMs. This includes both tools for the evaluation of LLMs and LLM-based applications with TruLens-Eval and deep learning explainability with TruLens-Explain.

?6.????? Vertex AI Studio (Google)

?You can evaluate the performance of foundation models and your tuned generative AI models on Vertex AI. The models are evaluated using a set of metrics against an evaluation dataset that you provide.

?7.????? Amazon Bedrock

?Amazon Bedrock supports model evaluation jobs. The results of a model evaluation job allow you to evaluate and compare a model's outputs, and then choose the model best suited for your downstream generative AI applications. Model evaluation jobs support common use cases for large language models (LLMs) such as text generation, text classification, question and answering, and text summarization.

?8.????? DeepEval (Confident AI)

An open-source LLM evaluation framework for LLM applications.

9.????? Promptfoo

A CLI and library for evaluating LLM output quality and performance, promptfoo enables you to systematically test prompts and models with predefined tests.

10.? UpTrain

UpTrain is an open source LLM Evaluation tool. It provides pre-built metrics to check LLM responses on aspects including correctness, hallucination and toxicity, among others.

11.? H2O LLM EvalGPT

This is an open tool for understanding a model’s performance across a plethora of tasks and benchmarks.

?Conclusion:

LLMs have indeed ushered in a new era of Artificial Intelligence, showcasing amazing capabilities across different tasks and domains. It is of paramount importance to evaluate the models across above mentioned aspects using proper tools, frameworks & benchmarks? so to harness their potential more effectively.