Q&A Assistant

DescriptionEvaluates an LLM's effectiveness as a team member in a business environment by assessing its ability to provide accurate and contextually relevant responses. It utilizes diverse queries covering both technical (such as coding) and non-technical areas.Number of Samples275LanguageEnglishProviderToqanEvaluation MethodAuto-evaluation with GPT4-Turbo over ground-truth.Data Collection PeriodFebruary 2022 - October 2023

Tags
Tags that describe the type of question asked.
Complexity
The complexity level of the questions.

Last updated: November 14, 2024

Share this view
#
Model
Provider
Size
Acceptance
No results.

Have a unique use-case you’d like to test?

We want to evaluate how LLMs perform on your specific, real world task. You might discover that a small, open-source model delivers the performance you need at a better cost than proprietary models. We can also add custom filters, enhancing your insights into LLM capabilities. Each time a new model is released, we'll provide you with updated performance results.

Leaderboard

An open-source model beating GPT-4 Turbo on our interactive leaderboard.

Don’t worry, we’ll never spam you.

Please, briefly describe your use case and motivation. We’ll get back to you with details on how we can add your benchmark.