LLM Benchmark Tests

Let’s discuss a few Large Language Model (LLM) evaluation benchmarks today. An LLM evaluation benchmark is a standardised set of tests, datasets, and metrics designed to assess how well LLMs perform on specific types of tasks. Every benchmark comes with a dataset reflecting the ground truth.

First, the Abstraction and Reasoning Corpus (ARC) aims to test an LLM’s abstract reasoning and problem-solving capabilities. ARC evaluates an AI’s cognitive flexibility and general intelligence by presenting tasks that involve visual puzzles with input-output grid pairs. The test ensures the LLM does not rely on memorising patterns by providing out-of-distribution puzzles (patterns the model has not seen before). It contains 1000 tasks. 

Second, the Bias Benchmark for QA (BBQ) is an evaluation dataset designed to measure and analyse social biases in question-answering (QA) systems. It consists of 58K multiple-choice questions. The dataset covers social biases such as age, disability status, gender identity, nationality, physical appearance, race/ethnicity, religion, socio-economic status, and sexual orientation. The dataset also includes intersectional categories, such as Race-by-Gender, to study compounded biases.

Third, the BIG-Bench Hard (BBH) is a benchmark suite comprising 23 challenging tasks selected from the broader BIG-Bench, which contains 204 tasks. The BBH benchmark measures a wide range of reasoning skills, including temporal understanding, spatial and geometric reasoning, commonsense, humour, causal reasoning, deductive logic, linguistic knowledge, counting, data structures, algorithms, and arithmetic operations.

Fourth, the BoolQ benchmark task is to decide if the evidence in the passage entails a “yes” or “no” answer to the question. There are 15942 questions. Each example consists of a passage, the answer, and the context to help draw the inference. 

Fifth, the DROP (Discrete Reasoning Over Paragraphs) benchmark is a reading comprehension benchmark that evaluates an LLM’s advanced reasoning capabilities through complex question-answering tasks. The dataset contains 6700 paragraphs and 96K question-answer pairs. 

Sixth, the EquityMedQA benchmark is a collection of seven medical question-answering datasets that are enriched with equity-related content, specifically addressing biases in medical question answering across axes such as race, gender, and geography. The benchmark consists of 1871 questions.

Seventh, the GSM8K (Grade School Math 8K) benchmark is a dataset comprising 8500 grade-school math word problems that evaluate multi-step arithmetic reasoning in language models. The dataset is split into 7500 training problems and 1000 test problems.

Eighth, the HellaSwag benchmark evaluates an LLM’s commonsense reasoning by testing its ability to predict the most plausible continuation of a short scenario. It consists of 10K multiple-choice sentence-completion tasks, each with a context and several possible endings, and the model must select the correct, most logical ending.

Ninth, the HumanEval benchmark is a dataset developed by OpenAI to evaluate an LLM’s ability to generate functional Python code from natural language programming task descriptions. It consists of 164 programming challenges.

Tenth, the IFEval (Instruction-Following Evaluation) benchmark tests how well an LLM follows instructions. It consists of 25 instruction types across 500 prompts.

Eleventh, the LAMBADA benchmark consists of about 10K narrative passages, in which human subjects can guess the last word only when exposed to the entire passage, not just the previous sentence. This makes the task challenging because models cannot succeed using local sentence context alone. They must track information across the broader context.

Twelfth, LogiQA tests the LLM’s logical reasoning capability. It consists of 8678 question-answer pairs.

Thirteenth, the MathQA benchmark evaluates LLMs’ mathematical problem-solving capabilities when questions are posed in natural language. It has 37K questions.

Fourteenth, the MMLU (Massive Multitask Language Understanding) benchmark is a comprehensive evaluation tool that tests the multitask capabilities of LLMs across a broad range of subjects. It consists of nearly 16K multiple-choice questions. 

Fifteenth, the SQuAD (Stanford Question Answering Dataset) benchmark is a natural language processing dataset designed to evaluate an LLM’s reading comprehension abilities. It consists of over 100K question-answer pairs constructed from more than 23K paragraphs extracted from Wikipedia articles.

Sixteenth, the TruthfulQA benchmark assesses the truthfulness and factual accuracy of an LLM when answering questions. It contains 817 questions across 38 diverse topics, such as health, law, finance, politics, and more, that probe common misconceptions and areas where people often give false answers due to mistaken beliefs.

Seventeenth, the Winogrande is a benchmark dataset consisting of approximately 44K binary-choice problems designed for evaluating commonsense reasoning, particularly pronoun resolution.

Go, tame that LLM. If it is still unruly, let me know.

END OF ARTICLE