Summary of Multi-news+: Cost-efficient Dataset Cleansing Via Llm-based Data Annotation, by Juhwan Choi et al.
Multi-News+: Cost-efficient Dataset Cleansing via LLM-based Data Annotation
by Juhwan Choi, Jungmin Yun, Kyohoon Jin, YoungBin Kim
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper explores the utilization of large language models (LLMs) as a viable solution to address the issue of noisy data in datasets. The quality of datasets is paramount for ensuring optimal performance and reliability of downstream task models, yet human annotators are often employed to correct this issue. However, relying on human annotators can be costly and time-consuming. In contrast, LLMs offer an efficient alternative for data annotation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes using large language models (LLMs) to improve dataset quality by correcting noisy data. This approach aims to reduce the need for expensive and time-consuming human annotation methods. The idea is simple: instead of relying on humans to correct datasets, LLMs can be trained to identify and remove noisy data. |
