Summary of The Effectiveness Of Llms As Annotators: a Comparative Overview and Empirical Analysis Of Direct Representation, by Maja Pavlovic et al.
The Effectiveness of LLMs as Annotators: A Comparative Overview and Empirical Analysis of Direct Representation
by Maja Pavlovic, Massimo Poesio
First submitted to arxiv on: 2 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 A comparative study of 12 Large Language Model (LLM) experiments investigates the effectiveness of LLMs in data annotation, highlighting both promising benefits and limitations. The models’ ability to reduce costs and time is demonstrated, but concerns about representativeness, bias, sensitivity to prompts, and English language preference remain. A separate empirical analysis examines the alignment between human-generated opinion distributions and GPT-generated opinions across four subjective datasets, supporting the need for diverse perspectives in data annotation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LLMs are super smart tools that help us with language-based tasks. This study looks at 12 different ways to use these models to label data. The good news is that they can save time and money! However, there are some problems too – like making sure the data is fair, not influenced by prompts, and only focused on English languages. Another part of the study compares what humans think with what a really smart computer program (GPT) thinks about four different topics. This shows us that we need to consider many different views when labeling data. |
Keywords
» Artificial intelligence » Alignment » Gpt » Large language model
