Summary of Evaluating the Performance Of Large Language Models For Sdg Mapping (technical Report), by Hui Yin et al.
Evaluating the Performance of Large Language Models for SDG Mapping (Technical Report)
by Hui Yin, Amir Aryani, Nakul Nambiar
First submitted to arxiv on: 5 Aug 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 study compares the performance of various open-source language models, including Mixtral, LLaMA 2, LLaMA 3, Gemma, Qwen2, and GPT-4o-mini, on the Sustainable Development Goal (SDG) mapping task. The researchers employed metrics such as F1 score, precision, and recall with micro-averaging to evaluate different aspects of the models’ performance. They found that LLaMA 2 and Gemma still have significant room for improvement, while the other four models do not exhibit particularly large differences in performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study compares big language models on a special task called SDG mapping. It looks at how well these models can help us understand important goals like climate change and education. The researchers use special metrics to see which models are better than others. They found that some models, like LLaMA 2 and Gemma, need more work, but the other models are pretty good. |
Keywords
» Artificial intelligence » F1 score » Gpt » Llama » Precision » Recall
