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Scalable Human Oversight for Aligned LLMs
webA 2025 peer-reviewed paper from Babcock University (Nigeria) proposing a hybrid oversight framework for LLM alignment; relevant to scalable oversight research but published in a mid-tier journal and warrants scrutiny of experimental rigor.
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Importance: 42/100journal articleprimary source
Summary
This paper proposes a Scalable Hybrid Oversight (SHO) framework combining selective human feedback, proxy reward modeling, behavioral auditing, and alignment metrics into a closed-loop system for LLM alignment. The framework addresses limitations of existing methods like SFT and RLHF, particularly high annotation costs and poor real-world generalization. Experiments across five datasets covering truthfulness, ethics, and adversarial prompts show SHO outperforms conventional approaches in safety and oversight efficiency.
Key Points
- •Proposes Scalable Hybrid Oversight (SHO) combining selective human feedback, proxy reward modeling, and behavioral auditing in a closed-loop alignment system.
- •Addresses key limitations of RLHF and SFT: high annotation costs, poor generalization to ethically sensitive or ambiguous real-world contexts.
- •Evaluated across five datasets including truthfulness, ethics, and adversarial prompts, outperforming conventional alignment baselines.
- •Introduces 'intent fidelity' as a core alignment metric, focusing on whether LLM outputs reliably reflect human values and intentions.
- •Targets sustainable, scalable deployment of LLMs in dynamic environments where oversight resources are constrained.
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| AI Alignment | Approach | 91.0 |
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Scalable Human Oversight for Aligned Large Language Models: A Hybrid Framework for Intent Fidelity | IIETA
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CiteScore 2024: 2.4 ℹ CiteScore:
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Scalable Human Oversight for Aligned Large Language Models: A Hybrid Framework for Intent Fidelity
Scalable Human Oversight for Aligned Large Language Models: A Hybrid Framework for Intent Fidelity
Folasade Y. Ayankoya * | Shade O. Kuyoro | Olubukola D. Adekola | Oluwasefunmi B. Famodimu
Department of Computer Science, Babcock University, Ilishan-Remo 121003, Nigeria
Department of Software Engineering, Babcock University, Ilishan-Remo 121003, Nigeria
Corresponding Author Email: ayankoyaf@babcock.edu.ng
Page: 2011-2020
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DOI: https://doi.org/10.18280/isi.300807
Received: 17 May 2025 |
Revised: 4 August 2025 |
Accepted: 16 August 2025 |
Available online: 31 August 2025 |
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© 2025 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license ( http://creativecommons.org/licenses/by/4.0/ ).
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