Contextual Literature-Based Discovery (C-LBD)

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Inspired from the idea that an AI assistant that can provide suggestions in plain English, including unique thoughts and connections.



Contextual Literature-Based Discovery (C-LBD) developed by researchers at the University of Illinois at Urbana-Champaign, the Hebrew University of Jerusalem, and the Allen Institute for Artificial Intelligence (AI2). C-LBD aims to address the limitations of traditional literature-based discovery (LBD) by using a natural language setting to constrain the generation space for LBD and generate sentences. The researchers introduce a novel modeling framework for C-LBD that can gather inspiration from disparate sources and use them to form novel hypotheses. They also introduce an in-context contrastive model to promote creative thinking. The team believes that expanding C-LBD to include a multimodal analysis of formulas, tables, and figures to provide a more comprehensive and enriched background context is an intriguing direction to investigate in the future. The use of advanced LLMs like GPT-4, which is currently in development, is another avenue to investigate. Can Language Models Generate New Scientific Ideas? Meet Contextualized Literature-Based Discovery (C-LBD) | Tanushree Shenwai - MarkTechPost

The assistant accepts as input (1) relevant information, such as present challenges, motives, and constraints, and (2) a seed phrase that should be the primary focus of the developed scientific concept. Given this information, C-LBD generates scientific hypotheses in natural language while grounding them in context that controls the hypothesis search space. C-LBD also takes into account critical context like experimental settings and background knowledge and motivations that human scientists. Two forms of C-LBD were investigated:

  • one that generates a full phrase explaining an idea
  • and another that generates only a salient component of the idea.

Literature-Based Discovery (LBD) in Healthcare