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Fine-tuning vs Prompting, Can Language Models Understand Human Values?

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arxiv 2403.09720 v1 pith:ANI2TIQN submitted 2024-03-12 cs.CL cs.AI

Fine-tuning vs Prompting, Can Language Models Understand Human Values?

classification cs.CL cs.AI
keywords languagemodelstaskfine-tuninghumanunderstandingvaluesaccurately
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Accurately handling the underlying support values in sentences is crucial for understanding the speaker's tendencies, yet it poses a challenging task in natural language understanding (NLU). In this article, we explore the potential of fine-tuning and prompt tuning in this downstream task, using the Human Value Detection 2023. Additionally, we attempt to validate whether models can effectively solve the problem based on the knowledge acquired during the pre-training stage. Simultaneously, our interest lies in the capabilities of large language models (LLMs) aligned with RLHF in this task, and some preliminary attempts are presented.

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Cited by 1 Pith paper

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  1. Beyond Independent Labels: Schwartz-Geometry Decoding for Human Value Detection

    cs.CL 2026-07 conditional novelty 6.0

    A Schwartz-aware energy decoder improves theory-coherent label sets on 19 refined values at no F1 cost, while training-time geometry and LLM prompting do not match it.