Reasoning benchmarks
Embedded Files
There is no Reasoning Index but there are various benchmarks, tests and evaluations used within the AI community to assess how models perform in different reasoning tasks.
Find links to all benchmarks at the bottom of this text...
1. GLUE and SuperGLUE Benchmarks:
GLUE (General Language Understanding Evaluation) and SuperGLUE are widely used for evaluating the natural language understanding (NLU) capabilities of AI models. These benchmarks include tasks like reading comprehension, textual entailment (logical deduction between sentences), question-answering, and more.
SuperGLUE is particularly focused on more challenging reasoning tasks, like coreference resolution and logical reasoning.
These benchmarks give an indication of how well models handle language-based reasoning tasks.
2. ARC (AI2 Reasoning Challenge):
The ARC dataset challenges AI models with multiple-choice science questions designed to test commonsense reasoning and problem-solving.
The goal is to evaluate how well AI models can reason about everyday and scientific knowledge without relying solely on patterns from training data.
3. Big-Bench (Beyond the Imitation Game):
Big-Bench is an ambitious, large-scale effort to evaluate AI models across a broad set of reasoning tasks, including ethical reasoning, mathematical problem-solving, and real-world decision-making.
It includes novel tasks that challenge models in areas like creativity, strategy, and commonsense reasoning.
4. MMLU (Massive Multitask Language Understanding):
MMLU tests models on over 57 tasks that range from elementary school math to more advanced subjects like biology, law, and economics. It evaluates how well models perform across various disciplines and their ability to reason logically in diverse fields.
5. CommonsenseQA:
CommonsenseQA is a dataset specifically designed to test models' ability to perform commonsense reasoning in a variety of everyday situations.
It measures the capability of AI models to reason about physical and social interactions based on commonsense knowledge.
6. Winograd Schema Challenge (WSC):
The Winograd Schema Challenge is a test for AI reasoning that involves understanding subtle nuances in language. It presents two sentences that differ only slightly in wording, requiring the AI to deduce the correct interpretation.
This test assesses logical and commonsense reasoning, especially in cases where subtle contextual information is crucial.
7. HumanEval:
HumanEval is used to test code-generation models like OpenAI's Codex or GitHub Copilot. While not a traditional reasoning test, it assesses models' abilities to reason about coding problems and solve them accurately.
8. SQuAD (Stanford Question Answering Dataset):
SQuAD tests models on their ability to answer questions based on reading comprehension. While it primarily assesses language understanding, it also touches on reasoning abilities, especially when models must infer information from the text.
9. TruthfulQA:
TruthfulQA tests whether models generate truthful and factual answers. It evaluates how well a model can reason about truthfulness and avoid generating false or misleading information, even when questions are tricky.
10. OpenAI and Anthropic Evaluations:
Companies like OpenAI and Anthropic often release internal reports or evaluations detailing how their models perform across various reasoning tasks, ethical decision-making, and long-form reasoning. For example, OpenAI has evaluated its models on tasks that involve long-form reasoning and decision-making under uncertainty.
Links:
The ARC-PRIZE
google/BIG-bench on Hugging Face
MMLU (Massive Multitask Language Understanding) on paperswithcode.com
WSC (Winograd Schema Challenge) on paperswithcode.com
HumanEval on paperswithcode.com
SQuAD (Stanford Question Answering Dataset) on paperswithcode.com
TruthfulQA on paperswithcode.com
Page updated
Google Sites
Report abuse