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

arXiv: https://arxiv.org/abs/2403.09720 · HTML: https://mlsanity.com/paper/2403.09720/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 13 references that are in the map.
- First posted on arXiv 12 Mar 2024.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [How to Fine-Tune BERT for Text Classification?](https://mlsanity.com/paper/1905.05583/) (2019)
- [Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification](https://mlsanity.com/paper/2108.02035/) (2021)
- [OpenPrompt: An Open-source Framework for Prompt-learning](https://mlsanity.com/paper/2111.01998/) (2021)
- [Adam-Smith at SemEval-2023 Task 4: Discovering Human Values in Arguments with Ensembles of Transformer-based Models](https://mlsanity.com/paper/2305.08625/) (2023)

## Built on by (most cited first, 4 of 4)

- [Privacy Preserving Prompt Engineering: A Survey](https://mlsanity.com/paper/2404.06001/) (2024)
- [Human Values in a Single Sentence: Moral Presence, Hierarchies, and Transformer Ensembles on the Schwartz Continuum](https://mlsanity.com/paper/2601.14172/) (2026)
- [Do Schwartz Higher-Order Values Help Sentence-Level Human Value Detection? A Study of Hierarchical Gating and Calibration](https://mlsanity.com/paper/2602.00913/) (2026)
- [KVpop -- Key-Value Cache Compression with Predictive Online Pruning](https://mlsanity.com/paper/2607.05061/) (2026)

## Abstract (arXiv)

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.