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Self-Comprehension for More Coherent Language Generation

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Artificial General Intelligence (AGI 2023)

Abstract

Linguoplotter is a distributed and chaotic architecture where an entanglement of different processes interact to generate a text describing a raw data input. This paper describes recent additions to the architecture whereby a greater degree of language comprehension is used to improve the coherence of generated text. Some examples of the architecture operating are considered, including where it performs well and generates a good quality text; and instances where it gets trapped in loops that either prevent an output from being generated or cause a lower quality output to be produced before there is a chance to find a better alternative. Finally, ideas from the program Metacat are considered which could allow the program to observe its own processes and become a more human-like intelligence.

Partially supported by the UK EPSRC under grants EP/R513106/1 (Wright), EP/S033564/1 and EP/W001632/1 (Purver); the Slovenian Research Agency via research core funding for the programme Knowledge Technologies (P2-0103) and the projects CANDAS (J6-2581) and SOVRAG (J5-3102).

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Notes

  1. 1.

    https://github.com/georgeawright/linguoplotter.

  2. 2.

    Using the version at https://github.com/georgeawright/linguoplotter/tree/v2.0.0.

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Correspondence to George A. Wright .

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Wright, G.A., Purver, M. (2023). Self-Comprehension for More Coherent Language Generation. In: Hammer, P., Alirezaie, M., Strannegård, C. (eds) Artificial General Intelligence. AGI 2023. Lecture Notes in Computer Science(), vol 13921. Springer, Cham. https://doi.org/10.1007/978-3-031-33469-6_33

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  • DOI: https://doi.org/10.1007/978-3-031-33469-6_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-33468-9

  • Online ISBN: 978-3-031-33469-6

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