Titlebook: Natural Scientific Language Processing and Research Knowledge Graphs; First International Georg Rehm,Stefan Dietze,Frank Krüger Conference

ANT

RTaC: A Generalized Framework for?Toolinging intricate tool sequencing with conditional and iterative logic. This research not only sets a new benchmark for tooling efficiency in LLMs but also opens new avenues for the application of LLMs in complex problem-solving scenarios, heralding a significant leap forward in the functionality and versatility of LLMs across diverse domains.

細(xì)頸瓶

騷擾

The Effect of?Knowledge Graph Schema on?Classifying Future Research Suggestionsves state of the art performance when combined with pretrained embeddings. Overall, we observe that schemas with limited variation in the resulting node degrees and significant interconnectedness lead to the best downstream classification performance.

認(rèn)為

burnish

badinage

Cytology

吞沒(méi)

OCR Cleaning of?Scientific Texts with?LLMs develop Large Language Models specially finetuned to correct OCR errors. We experimented with the mT5 model (both the mT5-small and mT5-large configurations), a Text-to-Text Transfer Transformer-based machine translation model, for the post-correction of texts with OCR errors. We compiled a paralle

occult

RTaC: A Generalized Framework for?Toolinghe dynamic selection and sequencing of tools in response to complex queries. Addressing this, we introduce Reimagining Tooling as Coding (RTaC), a groundbreaking framework that transforms tool usage into a coding paradigm. Inspired by recent advancements [.], RTaC conceptualizes tools as Python func

Original

Scientific Software Citation Intent Classification Using Large Language Modelshe introduction of new software systems. Despite its prevalence, there remains a significant gap in understanding how software is cited within the scientific literature. In this study, we offer a conceptual framework for studying software citation intent and explore the use of large language models,