ANT
发表于 2025-3-28 15:11:59
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.
细颈瓶
发表于 2025-3-28 19:49:23
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骚扰
发表于 2025-3-29 02:42:20
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.
认为
发表于 2025-3-29 03:51:44
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burnish
发表于 2025-3-29 09:05:13
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badinage
发表于 2025-3-29 14:39:03
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Cytology
发表于 2025-3-29 19:25:29
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吞没
发表于 2025-3-29 23:03:08
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
发表于 2025-3-30 01:33:47
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
发表于 2025-3-30 06:01:48
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,