mlsanityData to 7 Oct 2026

Can AI Extract Antecedent Factors of Human Trust in AI? An Application of Information Extraction for Scientific Literature in Behavioural and Computer Sciences

arXiv 2412.11344 ↗First posted 16 Dec 2024cs.CL cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

Information extraction from the scientific literature is one of the main techniques to transform unstructured knowledge hidden in the text into structured data which can then be used for decision-making in down-stream tasks. One such area is Trust in AI, where factors contributing to human trust in artificial intelligence applications are studied. The relationships of these factors with human trust in such applications are complex. We hence explore this space from the lens of information extraction where, with the input of domain experts, we carefully design annotation guidelines, create the first annotated English dataset in this domain, investigate an LLM-guided annotation, and benchmark it with state-of-the-art methods using large language models in named entity and relation extraction. Our results indicate that this problem requires supervised learning which may not be currently feasible with prompt-based LLMs.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 41 references that are in the map. First posted on arXiv 16 Dec 2024.

Builds on

18 of 41 references.

References · 18

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  3. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
  4. 2022Large Language Models are Few-Shot Clinical Information Extractorscited by 206
  5. 2020Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AIcited by 166
  6. 2020Named Entity Recognition as Dependency Parsingcited by 150
  7. 2023GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extractioncited by 116
  8. 2020SciREX: A Challenge Dataset for Document-Level Information Extractioncited by 111
  9. 2022Thinking about GPT-3 In-Context Learning for Biomedical IE? Think Againcited by 83
  10. 2023LLMaAA: Making Large Language Models as Active Annotatorscited by 78
  11. 2023LLMs Accelerate Annotation for Medical Information Extractioncited by 77
  12. 2023CodeIE: Large Code Generation Models are Better Few-Shot Information Extractorscited by 67
6 more
  1. 2019Neural Architectures for Nested NER through Linearizationcited by 56
  2. 2023Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation Extractorscited by 44
  3. 2023Retrieval-Augmented Code Generation for Universal Information Extractioncited by 40
  4. 2023Large Language Models as Annotators: Enhancing Generalization of NLP Models at Minimal Costcited by 33
  5. 2020The SOFC-Exp Corpus and Neural Approaches to Information Extraction in the Materials Science Domaincited by 32
  6. 2023Preserving Knowledge Invariance: Rethinking Robustness Evaluation of Open Information Extractioncited by 8

Built on by

Not cited yet.