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Language Model Sentence Completion with a Parser-Driven Rhetorical Control Method

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arxiv 2402.06125 v1 pith:G3HTQKCI submitted 2024-02-09 cs.CL

Joshua Zingale , Jugal Kalita This is my paper · ORCID
classification cs.CL
keywords modellanguagemethodparser-drivenrhetoricaltextaccessibleadherence
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Controlled text generation (CTG) seeks to guide large language model (LLM) output to produce text that conforms to desired criteria. The current study presents a novel CTG algorithm that enforces adherence toward specific rhetorical relations in an LLM sentence-completion context by a parser-driven decoding scheme that requires no model fine-tuning. The method is validated both with automatic and human evaluation. The code is accessible on GitHub.

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  1. Speeding up Speculative Decoding via Sequential Approximate Verification

    cs.LG 2025-02 conditional novelty 6.0 of 10

    A lightweight trained verifier sequentially accepts or rejects draft tokens, reducing calls to the target LLM and speeding up speculative decoding with minimal quality loss.

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