Overview

Abstract

While the superiority of language models over tra ditional search-engines has become clear in recent years, their ‘generativity’ remains a serious concern for policymakers. On the input side, it is generally acknowledged that most large models are trained on unethically sourced data. And on the output side, their tendency to hallucinate and misinform makes them unfit for domain-specific work. This paper takes the view that explainability and transparency cannot be achieved simply by putting ‘a human in the loop’. Taking legal work as a concrete site, it proposes Hyvmind – an architecture that puts ‘humans in the centre’ by recording and rewarding semantic labour through tokenised annotations. Its novelty lies in conceptualising legal research as a set of four interconnected functions (source, watch, frame and curate) around a common data-object (source-text). By storing and rewarding annotative-work through a distributed ledger system with nested states, it creates a secure, ethical and organic pathway for generating high-quality datasets for the next generation of domain-specific language models.

Keywords

tokenised annotations, semantic labour, reciprocal produsage, nested state, legal research, ontological plurality, distributed ledger

Abbreviations

Abbreviation	Full Form
AGLI	Artifically Generated Legal Information
AVC	Annotation Value Chain
CC	Curation Contract
CID	Content Identifier
CNFT	Curation NFT
FNFT	Frame NFT
FT	Fungible Token
HDAO	Hyvmind’s Decentralised Autonomous Organisation
IAA	Inter Annotator Agreement
ILP	Intellectual Labour Practice
LC	Location Contract
LNFT	Link NFT
LO	Legal Ontologies
NFT	Non Fungible Token
NLP	Natural Language Processing
NNFT	Nested NFT
ST	Source Text
TNFT	Text NFT
UTS	Utility Token System
UVS	Unrealised Value Space