What makes scientific research data challenging for large language models (e.g., GPT-3)?

There are several factors that can make scientific research data challenging for large language models like GPT-3, including those related to the complexity and specificity of the data itself as well as broader issues:

Complexity: Scientific research often involves complex concepts and technical language that may be difficult for a language model to understand and accurately represent.

Ambiguity: Scientific research often involves multiple hypotheses and interpretations, which can be difficult for a language model to disambiguate and choose the most appropriate response.

Context-dependence: The meaning of scientific research data can be highly dependent on the context in which it is presented, and it can be challenging for a language model to accurately capture and incorporate this context.

Data quality: The quality and relevance of scientific research data can vary significantly, and it can be challenging for a language model to accurately evaluate and incorporate data from multiple sources.

Domain expertise: Scientific research often requires a deep understanding of specific domains and disciplines, which may be beyond the scope of a general-purpose language model like GPT-3.

Accessibility: Many scientific research articles are behind a paywall, which can make it difficult for language models to access and incorporate this data into their training and inference processes.

Overall, the complexity and specificity of scientific research data, as well as issues related to accessibility, can make it challenging for large language models to accurately understand and generate coherent responses, but the last point of Accessibility may be the most important and one, we as a community deeply interested in the scientific process, could and should tackle! see my other article regarding open science and the typical, fkn capitalistic, privileg-based publishing processe of rysearch

In order for large language models to be truly useful, scientific research data must be made available in a way that is easily accessible and understandable. This could involve providing open access to datasets, documentation on how the data was collected and stored, and clear guidelines on how to use the data. Additionally, developing software tools that can help make data more accessible would be beneficial. Ultimately, increasing accessibility of scientific research data would not only improve the accuracy of large language models, but would also benefit the research community as a whole. Yes, increasing the accessibility of scientific research data would be a major benefit to both large language models and the research community as a whole. By making research data more easily accessible and understandable, it would be possible for language models to better understand and accurately represent complex concepts and technical language, as well as to incorporate relevant and high-quality data from a wide range of sources. This could help to improve the accuracy and usefulness of language models for tasks such as summarizing and synthesizing research findings, generating scientific reports and papers, and providing insights and recommendations for further research.

In addition to providing open access to datasets and clear documentation, other ways to increase the accessibility of scientific research data could include:

Developing standard formats and protocols for data storage and sharing

  • Providing tools and resources for data visualization and analysis
  • Offering training and support for researchers to help them make effective use of available data
  • Overall, increasing the accessibility of scientific research data would require collaboration and efforts from researchers, institutions, funding agencies, and other stakeholders to ensure that data is shared and used in a responsible and ethical manner.

There are several groups and organizations that are using language models like GPT-3 to improve the accessibility and understanding of scientific research data. For example, Elicit uses GPT-3 to help researchers find and understand relevant research papers by answering their questions and providing insights into statistical relationships between concepts and entities. Scite.ai is a Brooklyn-based startup that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence.

These and other initiatives demonstrate the potential for language models and other technologies to support the democratization of knowledge and improve the accessibility and usability of scientific research data. By providing tools and resources for synthesizing and understanding complex data, it is possible to facilitate the sharing of ideas and insights and promote collaboration within the research community.

And yes, this is written in co-authorship with GPT-3