HelloBench - An open-source benchmark tool for evaluating the long text generation capabilities of LLMs.
HelloBench is an open-source benchmark for evaluating the long text generation capabilities of large language models (LLMs). HelloBench includes five subtasks based on Bloom's Taxonomy: open-ended question answering, summarizing, chat, text...
What is HelloBench?
HelloBench is an open-source benchmark for evaluating the long text generation capabilities of large language models (LLMs). HelloBench includes five subtasks based on Bloom's Taxonomy: open-ended question answering, summarizing, chat, text completion, and heuristic text generation. HelloBench uses real-world data such as Quora and Reddit to ensure task diversity and practicality. It introduces HelloEval, an efficient evaluation method that reduces the burden of human evaluation while maintaining high relevance to human assessments. Experiments on multiple LLMs show that existing models face challenges in generating long texts exceeding 4000 words.
HelloBench's main functions
- Layered task designHelloBench divides the long text generation task into five sub-tasks based on Bloom's taxonomy, with each sub-task targeting different language model capabilities.
- Real datasetsThe dataset is built based on real data from platforms such as Quora and Reddit to ensure the practicality and diversity of the evaluation.
- Automated evaluationThe HelloEval method is used to automatically evaluate the long text generation capabilities of LLMs, reducing the time and effort required for manual evaluation.
- Comparison of assessment methodsThe comparison demonstrates the relevance of HelloEval to human assessment by comparing it with traditional assessment metrics such as ROUGE and BLEU.
HelloBench's technical principles
- Bloom's TaxonomyBased on Bloom's taxonomy, long text generation tasks are divided into different levels, corresponding to different cognitive complexities.
- Dataset ConstructionManually collect and filter internet data to build high-quality, diverse datasets.
- HelloEval evaluation methodDesign checklists and collect human-labeled data, then use linear regression analysis to determine the weighted scores for the checklists.
- LLM-as-a-JudgeBased on a language model as the evaluator, the system answers checklist questions to assess the quality of the generated text.
- Linear regression analysisLinear regression analysis was performed on manually labeled data to obtain weighted scores that align with human assessments.
- Error Mode AnalysisThis study analyzes common errors of LLMs in long text generation and identifies the limitations of these models.
HelloBench project address
- GitHub repository:https://github.com/Quehry/HelloBench
- HuggingFace model library:https://huggingface.co/papers/2409.16191
- arXiv technical paper:https://arxiv.org/pdf/2409.16191
HelloBench application scenarios
- Language model developmentDevelopers used HelloBench to evaluate and compare the performance of different language models on long text generation tasks.
- academic researchResearchers used HelloBench to conduct experiments related to long text generation, and may publish academic papers or conduct further research.
- Product TestingWhen developing new AI products or services, companies use HelloBench to test and optimize the text generation capabilities of their products.
- Educational assessmentEducational institutions use HelloBench to assess and improve the text generation quality of teaching aids.
- Content creationContent creators use HelloBench to evaluate and improve automated content generation tools, such as automated writing and blog post generation.
- Dialogue system: Evaluate and improve the performance of chatbots or virtual assistants in long conversations.