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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.
Metadata
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
Scalable Human Oversight for Aligned Large Language Models: A Hybrid Framework for Intent Fidelity
Folasade Y. Ayankoya\*[](https://orcid.org/0000-0003-0308-2753) **\|** Shade O. Kuyoro[](https://orcid.org/0000-0001-7235-7744) **\|** Olubukola D. Adekola[](https://orcid.org/0000-0002-5495-6791) **\|** Oluwasefunmi B. Famodimu[](https://orcid.org/0009-0005-7129-4899)
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
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Revised:
4 August 2025
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Accepted:
16 August 2025
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Available online:
31 August 2025
\|Citation
© 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/](http://creativecommons.org/licenses/by/4.0/)).
[](https://www.iieta.org/download/file/fid/185099)
OPEN ACCESS
Abstract:
Large language models (LLMs) exhibit impressive linguistic and reasoning abilities, yet they frequently produce outputs that deviate from human intent, especially in ethically sensitive or ambiguous contexts. Current alignment methods, such as supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF), offer partial solutions but are limited by high annotation costs and poor generalization to real-world scenarios. This paper proposes a scalable hybrid oversight (SHO) framework that combines selective human feedback, proxy reward modeling, behavioral auditing, and alignment metrics into a closed-loop system for intent fidelity. Our experiments across five datasets including truthfulness, ethics, and adversarial prompts demonstrate that SHO outperforms the conventional approaches in safety, alignment, and oversight efficiency. This work provides a path toward sustainable, high-integrity deployment of LLMs in dynamic environments.
Keywords:
_AI alignment, behavioral auditing, ethical AI, human oversight, intent fidelity, large language models, reward modeling, scalable supervision_
1\. Introd
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