Vocabulary adaptation via targeted token addition and replacement improves semantic similarity, domain word usage, and training efficiency for LLM summarization in legal and medical domains.
Proceedings of the Nineteenth International Conference on Artificial Intelligence and Law , pages =
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A neuro-symbolic system converts legal clauses into deterministic typed graphs for consistent, auditable adjudication that cuts compute costs by over 90% versus direct large reasoning model use.
citing papers explorer
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Learning Faster with Better Tokens: Parameter-Efficient Vocabulary Adaptation for Specialized Text Summarization
Vocabulary adaptation via targeted token addition and replacement improves semantic similarity, domain word usage, and training efficiency for LLM summarization in legal and medical domains.
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Accurate Legal Reasoning at Scale: Neuro-Symbolic Offloading and Structural Auditability for Robust Legal Adjudication
A neuro-symbolic system converts legal clauses into deterministic typed graphs for consistent, auditable adjudication that cuts compute costs by over 90% versus direct large reasoning model use.