Why PyBench? The LLM Agent, equipped with a code interpreter, is capable of automatically solving real-world coding tasks, such as data analysis and image processing. % However, existing benchmarks primarily focus on either simplistic tasks, such as completing a few lines of code, or on extr...
These benchmarks consist of sample data, a set of questions or tasks to test LLMs on specific skills, metrics for evaluating performance and a scoring mechanism. Models are benchmarked based on their capabilities, such as coding, common sense and reasoning. Other capabilities encompass natural ...
指令跟随(Instruction Following) 鲁棒性(Robustness) 偏见(Bias) 幻觉(Hallucinations) 安全性(Safety) 例:GPT-4 vs LLaMA2-7B能力维度对比评测 1. 自动评估方法 模型效果评估 基准和指标(Benchmarks & Metrics) Rule-based自动评测 基本流程 根据数据集原始question来构建prompt 示例(few-shot) 示例:few-shot wit...
#假如当前本地工作路径为 /path/to/workdir wget https://modelscope.oss-cn-beijing.aliyuncs.com/open_data/benchmark/data.zip unzip data.zip 则解压后的数据集路径为:/path/to/workdir/data 目录下,该目录在后续步骤将会作为--dataset-dir参数的值传入 使用本地数据集创建评估任务 python llmuses/run.py...
专业能力(e.g. coding、math) 应用能力(MedicalApps、AgentApps、AI-FOR-SCI ...) 指令跟随(Instruction Following) 鲁棒性(Robustness) 偏见(Bias) 幻觉(Hallucinations) 安全性(Safety) 例:GPT-4 vs LLaMA2-7B能力维度对比评测 1. 自动评估方法 模型效果评估 基准和指标(Benchmarks & Metrics) 数据...
4.1 AI for coding 带来编程能力民主化 代码开发是最近 AI 提升最大、热度最高的领域,背后最重要的原因就是 sonnet3.5 的发布带来的推理能力的提升。这个提升最直接的 benchmark 就是写出可靠代码的行数:原本 4o 只能可以写 20 行可靠的代码,Sonnet 3.5 可以写 200 行。
专业能力(e.g. coding、math) 应用能力(MedicalApps、AgentApps、AI-FOR-SCI …) 指令跟随(Instruction Following) 鲁棒性(Robustness) 偏见(Bias) 幻觉(Hallucinations) 安全性(Safety) 例:GPT-4 vs LLaMA2-7B能力维度对比评测 1. 自动评估方法 模型效果评估 基准和指标(Benchmarks & Metrics) 数...
Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering by Pan et al. in The 36th Conference on Neural Information Processing Systems (NeurIPS) (2022) Did aristotle use a laptop? a question answering benchmark with implicit reasoning strategies. by Geva et al. in...
应用能力(MedicalApps、AgentApps、AI-FOR-SCI ...) 指令跟随(Instruction Following) 鲁棒性(Robustness) 偏见(Bias) 幻觉(Hallucinations) 安全性(Safety) 例:GPT-4 vs LLaMA2-7B能力维度对比评测 1. 自动评估方法 模型效果评估 基准和指标(Benchmarks & Metrics) ...
We analyzed results from a number of research teams, focusing on an algorithm’s ability to do well on the widely used HumanEval coding benchmark. You can see our findings in the diagram below. GPT-3.5 (zero shot) was 48.1% correct. GPT-4 (zero shot) does better at 67.0%. However...