Fully automatic extraction of morphological traits from the Web: utopia or reality?
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Abstract (arXiv)
Plant morphological traits, their observable characteristics, are fundamental to understand the role played by each species within their ecosystem. However, compiling trait information for even a moderate number of species is a demanding task that may take experts years to accomplish. At the same time, massive amounts of information about species descriptions is available online in the form of text, although the lack of structure makes this source of data impossible to use at scale. To overcome this, we propose to leverage recent advances in large language models (LLMs) and devise a mechanism for gathering and processing information on plant traits in the form of unstructured textual descriptions, without manual curation. We evaluate our approach by automatically replicating three manually created species-trait matrices. Our method managed to find values for over half of all species-trait pairs, with an F1-score of over 75%. Our results suggest that large-scale creation of structured trait databases from unstructured online text is currently feasible thanks to the information extraction capabilities of LLMs, being limited by the availability of textual descriptions covering all the traits of interest.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 12 of its 42 references that are in the map. First posted on arXiv 23 Sep 2024.
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12 of 42 references.
References · 12
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2022Training language models to follow instructions with human feedbackcited by 15,971
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
- 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
- 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,307
- 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,218
- 2019Language Models as Knowledge Bases?cited by 1,936
- 2023Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Modelscited by 698
- 2014Training Deep Neural Networks on Noisy Labels with Bootstrappingcited by 322
- 2023ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPTcited by 250
- 2019Commonsense Knowledge Mining from Pretrained Modelscited by 189
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