Do Transformers Encode a Foundational Ontology? Probing Abstract Classes in Natural Language

Jullien, Mael; Valentino, Marco; Freitas, André Victor Lucci

doi:10.48550/arxiv.2201.10262

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…Natural Language Processing (NLP) Algorithms B. Classification Algorithms C. Artificial Theorist Algorithms 14 See Jullien et al (2022) an example of work in this direction, however. 15 See Russell and Norvig (2020) for AI applications that can be used in each of these processes and see Zuboff (2019) for examples of AI applications used in surveillance capitalism.…”

Section: The "Ultimate Dictionary" Appmentioning

confidence: 99%

The End of Vagueness: Technological Epistemicism, Surveillance Capitalism, and Explainable Artificial Intelligence

Kerr

Scharp

2022

Minds & Machines

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Artificial Intelligence (AI) pervades humanity in 2022, and it is notoriously difficult to understand how certain aspects of it work. There is a movement—Explainable Artificial Intelligence (XAI)—to develop new methods for explaining the behaviours of AI systems. We aim to highlight one important philosophical significance of XAI—it has a role to play in the elimination of vagueness. To show this, consider that the use of AI in what has been labeled surveillance capitalism has resulted in humans quickly gaining the capability to identify and classify most of the occasions in which languages are used. We show that the knowability of this information is incompatible with what a certain theory of vagueness—epistemicism—says about vagueness. We argue that one way the epistemicist could respond to this threat is to claim that this process brought about the end of vagueness. However, we suggest an alternative interpretation, namely that epistemicism is false, but there is a weaker doctrine we dub technological epistemicism, which is the view that vagueness is due to ignorance of linguistic usage, but the ignorance can be overcome. The idea is that knowing more of the relevant data and how to process it enables us to know the semantic values of our words and sentences with higher confidence and precision. Finally, we argue that humans are probably not going to believe what future AI algorithms tell us about the sharp boundaries of our vague words unless the AI involved can be explained in terms understandable by humans. That is, if people are going to accept that AI can tell them about the sharp boundaries of the meanings of their words, then it is going to have to be XAI.

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Section: The "Ultimate Dictionary" Appmentioning

confidence: 99%

The End of Vagueness: Technological Epistemicism, Surveillance Capitalism, and Explainable Artificial Intelligence

Kerr

Scharp

2022

Minds & Machines

View full text Add to dashboard Cite

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“…Hallucinations tend to surface prominently in domain-specific discourse, including the sciences. This elicits the need to establish the ability of these models to encode and preserve factual knowledge as well as establishing methodologies for the critical evaluation of its representation properties [15,16,17].…”

Section: Introductionmentioning

confidence: 99%

Large Language Models, scientific knowledge and factuality: A systematic analysis in antibiotic discovery

Wysocka

Wysocki

Delmas

et al. 2023

Preprint

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Background Inferring over and extracting information from Large Language Models (LLMs) trained on a large corpus of scientific literature can potentially drive a new era in biomedical research, reducing the barriers for accessing existing medical evidence. This work examines the potential of LLMs for dialoguing with biomedical background knowledge, using the context of antibiotic discovery as an exemplar motivational scenario. The context of biomedical discovery from natural products entails understanding the relational evidence between an organism (e.g. a Fungi such as Albifimbria verrucaria), an associated chemical (Verrucarin A) and its associated antibiotic properties (present antibiotic activity). Results This work provides a systematic assessment on the ability of LLMs to encode and express these relations, verifying for fluency, prompt-alignment, semantic coherence, factual knowledge and specificity of generated responses. The systematic analysis is applied to nine state-of-the-art models, from models specialised on biomedical scientific corpora to general models such as ChatGPT and GPT-4 in two prompting-based tasks: chemical compound definition generation and chemical compound-fungus relation determination. Results show that while recent models have improved in fluency, factual accuracy is still low and models are biased towards over-represented entities. The ability of LLMs to serve as biomedical knowledge bases is questioned, and the need for additional systematic evaluation frameworks is highlighted. The best performing GPT-4 produced a factual definition for 70% of chemical compounds and 43.6% factual relations to fungi, whereas the best open source model BioGPT-large 30% of the compounds and 30% of the relations for the best-performing prompt. Conclusions The results show that while LLMs are currently not fit for purpose to be used as biomedical factual knowledge bases, there is a promising emerging property in the direction of factuality as the models become domain specialised, scale-up in size and level of human feedback.

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Explainable Authorship Identification in Cultural Heritage Applications

Setzu,

Corbara,

Monreale

et al. 2024

J. Comput. Cult. Herit.

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While a substantial amount of work has recently been devoted to improving the accuracy of computational Authorship Identification (AId) systems for textual data, little to no attention has been paid to endowing AId systems with the ability to explain the reasons behind their predictions. This substantially hinders the practical application of AId methods, since the predictions returned by such systems are hardly useful unless they are supported by suitable explanations. In this paper, we explore the applicability of existing general-purpose eXplainable Artificial Intelligence (XAI) techniques to AId, with a focus on explanations addressed to scholars working in cultural heritage. In particular, we assess the relative merits of three different types of XAI techniques (feature ranking, probing, factual and counterfactual selection) on three different AId tasks (authorship attribution, authorship verification, same-authorship verification) by running experiments on real AId textual data. Our analysis shows that, while these techniques make important first steps towards explainable Authorship Identification, more work remains to be done in order to provide tools that can be profitably integrated in the workflows of scholars.

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Do Transformers Encode a Foundational Ontology? Probing Abstract Classes in Natural Language

Cited by 3 publications

References 24 publications

The End of Vagueness: Technological Epistemicism, Surveillance Capitalism, and Explainable Artificial Intelligence

The End of Vagueness: Technological Epistemicism, Surveillance Capitalism, and Explainable Artificial Intelligence

Large Language Models, scientific knowledge and factuality: A systematic analysis in antibiotic discovery

Explainable Authorship Identification in Cultural Heritage Applications

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