Graphs, LLM's and Science of Science
Graphs and LLM’s The evolving nature of experiments in artificial intelligence and the exponential pace of scientific progress in developing domain specific large language models (LLM) elevated the use cases of language models to assist researchers in advancing scientific discovery. Conversational interfaces using Auto-regressive LLM’s such as GPT-4, LLaMA, Gemini, Claude, Mistral dominated the public discourse with immediate adoption by diverse communities. Knowledge distillation from representations of LLM’s are good priors for evaluating predictive models large for AI for Science(AI4Science) initiative and scientific discovery in the age of artificial intelligence will rely on such initiatives. Scientific discovery includes several stages and collecting the data, building the experiments, analyzing the results to come up with salient hypothesis are few of the stages that have reasonable scope to include LLM’s in the loop. Augmenting various stages of the scientific process with AI models comes with a plethora of benefits and poses risks therefore it is important to make reliability, and safety of these models a priority thereby enhancing the societal benefit of scientific discovery. ...