Summary of Prompt-based Vs. Fine-tuned Llms Toward Causal Graph Verification, by Yuni Susanti et al.
Prompt-based vs. Fine-tuned LLMs Toward Causal Graph Verification
by Yuni Susanti, Nina Holsmoelle
First submitted to arxiv on: 29 May 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 This paper explores the application of natural language processing (NLP) technology for automatic verification of causal graphs using text sources. The goal is to utilize Large Language Models (LLMs) such as BERT and ChatGPT to predict whether a causal relation can be observed between node pairs based on textual context, potentially replacing manual evaluation by human experts. The study compares the performance of two types of NLP models: pre-trained language models fine-tuned for causal relation classification tasks and prompt-based LLMs. Preliminary experiments on biomedical and open-domain datasets show that fine-tuned models outperform prompt-based LLMs, achieving up to 20.5 points improvement in F1 score. The code and pre-processed datasets are shared in the repository. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using computers to help us understand cause-and-effect relationships in text data. Currently, experts need to manually check these relationships, but this can be time-consuming and prone to error. The idea is to use special AI models that are trained on large amounts of text data to predict whether a cause-and-effect relationship exists between two things based on what’s written about them. The researchers tested different types of AI models to see which ones work best for this task. They found that the models that were specifically trained for this task performed much better than those that weren’t. This could be useful in many fields, such as medicine and science. |
Keywords
» Artificial intelligence » Bert » Classification » F1 score » Natural language processing » Nlp » Prompt
