LMentry: A Language Model Benchmark of Elementary Language Tasks

By Avia Efrat, Or Honovich, Omer Levy. 2022

33 citations recorded by the source corpus.

As the performance of large language models rapidly improves, benchmarks are getting larger and more complex as well. We present LMentry, a benchmark that avoids this"arms race"by focusing on a compact set of tasks that are trivial to humans, e.g. writing a sentence containing a specific word, identifying which words in a list belong to a specific category, or choosing which of two words is longer. LMentry is specifically designed to provide quick and interpretable insights into the capabilities and robustness of large language models. Our experiments reveal a wide variety of failure cases that, while immediately obvious to humans, pose a considerable challenge for large language models, including OpenAI's latest 175B-parameter instruction-tuned model, TextDavinci002. LMentry complements contemporary evaluation approaches of large language models, providing a quick, automatic, and easy-to-run"unit test", without resorting to large benchmark suites of complex tasks.

Subfields: Artificial Intelligence, Natural Language Processing, Evaluation & Benchmarks

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Natural Language Processing

In Evaluation & Benchmarks

Citations in the corpus

Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor (2022, methodology): “…T5 model (Raffel et al., 2020) on Unnatural Instructions can outperform both T0++ (Sanh et al., 2021) and Tk-Instruct (Wang et al., 2022) across several benchmarks, including SuperNatural Instructions (Wang et al., 2022), BIGbench Hard (Suzgun et al., 2022), and LMentry (Efrat et al., 2022).”