Open-source alternatives to MMCU

CMMLU: Measuring massive multitask language understanding in Chinese

An open science effort to benchmark legal reasoning in foundation models

A Large Language Model for IT Operations

This repository provides scripts for evaluating NLP models on the LEXTREME benchmark, a set of diverse multilingual tasks in legal NLP

PromptCBLUE: a large-scale instruction-tuning dataset for multi-task and few-shot learning in the medical domain in Chinese

Xiezhi (獬豸) is a comprehensive evaluation suite for Language Models (LMs). It consists of 249587 multi-choice questions spanning 516 diverse disciplines and four difficulty levels, as shown below. Please check our paper for more details, and our website will be open later on.

Benchmarking Legal Knowledge of Large Language Models

Abstract: FinanceBench is a first-of-its-kind test suite for evaluating the performance of LLMs on open book financial question answering (QA). This repository contains an open source sample of 150 annotated examples used in the evaluation and analysis of models assessed in the FinanceBench…

This repository contains information about AGIEval, data, code and output of baseline systems for the benchmark.

When FLUE Meets FLANG: Benchmarks and Large Pretrained Language Model for Financial Domain

Official github repo for C-Eval, a Chinese evaluation suite for foundation models NeurIPS 2023

论文链接：https://arxiv.org/abs/2302.09432

The FinEval financial domain evaluation benchmark, based on quantitative fundamental methods and developed through long-term objective research, summarization, and rigorous manual screening, utilizes over 26,000 diverse question types that are highly consistent with real-world application scenarios.

Chinese Financial Assistant Benchmark for Large Language Model