Open-source alternatives to Codefuse Devops Eval

Chinese Generation Evaluation

MEASURING MASSIVE MULTITASK CHINESE UNDERSTANDING

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

Mr. Ranedeer AI Tutor is an AI education framework and system prompt designed to transform a large language model into a personalized tutor. It uses a structured set of instructions to organize educational content into sequential modules and knowledge assessments for adaptive learning. The system features a persona template that allows for the adjustment of academic depth and communication tone to match a student's specific needs. It also provides multilingual support, enabling the tutor to switch instruction and output languages based on user preferences. The framework covers custom lesson

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