An Operational Guide to Enhancing Your Academic Essay Writing Skills
WritingBench provides a systematic testing and optimization solution for solving the problem of structured representation and terminological accuracy of large models in academic writing:
- Task-specific screening: Filtering tasks in the "academic" domain from benchmark_all.jsonl (about 1/6 of the total number of tasks), focusing on training models to deal with specialized scenarios such as literature reviews and methodology descriptions.
- Application of scoring criteria: Analyze model weaknesses via scores.jsonl using 5 dynamic scoring criteria (e.g., citation normality, terminology accuracy, etc.)
- Mixed assessment strategy: Balance efficiency and accuracy by first using a specialized judging model (Qwen-7B) for basic scoring and then manually spot-checking key metrics
- Utilization of reference materials: Adjust the model output format in conjunction with academic templates (e.g., IEEE paper formats) included in the task package
Implementation recommendation: prioritize the use of the categorization subset under benchmark_query/requirement/, and gradually improve the model capability through progressive training (from 100-word summaries to full chapter writing).
This answer comes from the articleWritingBench: a benchmarking assessment tool to test the writing skills of large modelsThe
May not be reproduced without permission:AI productivity tools " How to use WritingBench to improve the performance of big models in the field of academic essay writing?
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