mlsanityData to 7 Oct 2026

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

arXiv 2403.09720 ↗First posted 12 Mar 2024cs.CL cs.AIcited by 4

Not on the map. Why?

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 9 of its 13 references that are in the map. First posted on arXiv 12 Mar 2024.

Builds on

9 of 13 references.

References · 9

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  3. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  4. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,222
  5. 2021SimCSE: Simple Contrastive Learning of Sentence Embeddingscited by 2,168
  6. 2019How to Fine-Tune BERT for Text Classification?cited by 409
  7. 2021Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classificationcited by 183
  8. 2021OpenPrompt: An Open-source Framework for Prompt-learningcited by 148
  9. 2023Adam-Smith at SemEval-2023 Task 4: Discovering Human Values in Arguments with Ensembles of Transformer-based Modelscited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 3.

4 papers.

  1. 2024Privacy Preserving Prompt Engineering: A Surveycited by 24
  2. 2026Human Values in a Single Sentence: Moral Presence, Hierarchies, and Transformer Ensembles on the Schwartz Continuumcited by 3
  3. 2026Do Schwartz Higher-Order Values Help Sentence-Level Human Value Detection? A Study of Hierarchical Gating and Calibrationcited by 3
  4. 2026KVpop -- Key-Value Cache Compression with Predictive Online Pruningcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2024Are the Values of LLMs Structurally Aligned with Humans? A Causal Perspective
  2. 2025Fine-tuning on simulated data outperforms prompting for agent tone of voice
  3. 2024Can DPO Learn Diverse Human Values? A Theoretical Scaling Law
  4. 2025What Makes a Good Natural Language Prompt?
  5. 2023An Emulator for Fine-Tuning Large Language Models using Small Language Models
  6. 2023Psychometric Predictive Power of Large Language Models
  7. 2024Scaling Data Diversity for Fine-Tuning Language Models in Human Alignment
  8. 2025Internal Value Alignment in Large Language Models through Controlled Value Vector Activation