Summary of Integrating Expert Labels Into Llm-based Emission Goal Detection: Example Selection Vs Automatic Prompt Design, by Marco Wrzalik et al.
Integrating Expert Labels into LLM-based Emission Goal Detection: Example Selection vs Automatic Prompt Design
by Marco Wrzalik, Adrian Ulges, Anne Uersfeld, Florian Faust
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 paper proposes a machine learning-based solution for detecting emission reduction goals in corporate reports, a crucial task for monitoring companies’ progress in addressing climate change. Specifically, it explores two strategies for integrating expert feedback into language model-based pipelines: dynamic selection of few-shot examples and automatic optimization of the prompt by the language model itself. The authors compare these approaches on a public dataset of 769 climate-related passages from real-world business reports and find that automatic prompt optimization is the superior approach. However, combining both methods provides only limited benefits. Qualitative results suggest that optimized prompts effectively capture the intricacies of the targeted emission goal extraction task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us understand how to better detect when companies are reducing their emissions. It’s an important task because it helps us track if companies are keeping their promises to reduce pollution and address climate change. The authors test two different ways of improving language models’ ability to extract this information from company reports: selecting a few example passages that are similar to the ones being analyzed, and letting the model adjust its own questions based on the feedback. They find that letting the model adjust its own questions works better than selecting examples. This is good news because it means we can trust the models more when they’re analyzing company reports. |
Keywords
» Artificial intelligence » Few shot » Language model » Machine learning » Optimization » Prompt
