Automating Causal Relation Extraction in Scientific Texts using Large Language Models

The article discusses the challenge of extracting cause-and-effect relationships from scientific texts, particularly in the medical domain. The task is crucial for understanding the underlying mechanisms of diseases and developing effective treatments. However, manual annotation of causal statements in scientific literature is time-consuming and expensive, limiting the scope of large-scale studies. To address this issue, the authors propose using large language models (LLMs) to automate the cause-and-effect relationship extraction task.
The authors explain that LLMs are trained on vast volumes of textual data and can understand and generate human-like text based on prompts. By standard prompting techniques, they force the answer to be only based on the source document, without discovering new knowledge. The goal is to combine and standardize already existing information to identify cause-and-effect relationships between entities in scientific texts.
The authors then present a case study using medical abstracts to demonstrate the effectiveness of their approach. They analyze the causal statements in an abstract and use a quadratic-time, iterated pairwise approach to match entities with similar meanings. The small number of entities extracted from each abstract allows for executing the approach within a limited time frame.
The authors highlight some challenges in extracting cause-and-effect relationships, such as dealing with synonyms or redundant entities, and ensuring the accuracy of the causal connections. They also emphasize the importance of using large language models, which are trained in an unsupervised manner, to overcome these difficulties.
In conclusion, the article presents a novel approach to automating cause-and-effect relationship extraction in scientific texts, particularly in the medical domain. By leveraging the power of LLMs, the authors aim to facilitate large-scale studies that can provide valuable insights into the underlying mechanisms of diseases and guide the development of effective treatments. The proposed approach has the potential to demystify complex concepts by using everyday language and engaging metaphors or analogies, making it more accessible to a wider audience.