Scalable Human Oversight for Aligned Large Language Models: A Hybrid Framework for Intent Fidelity

The fast-growing large language models have put AI into widespread use across various industries, including customer service, medicine and law. The technological advancement resulted in language models, such as the GPT series by OpenAI, and Google's Gemini, showing exceptional skill in language comprehension and language generation. Within this larger deployment of the systems, there are concerns about whether these systems reliably reflect human values and intentions [1, 2].

The major challenge with the systems is that of value consistency, which is ensuring these systems function in accordance with human intent while minimizing unintended harm [3]. Unlike traditional AI built on explicit programming rules, LLMs train on vast unrefined substandard datasets, which can result into unpredictable and difficult-to-classify behaviors [4]. Misalignments are now obvious: biased outputs that misrepresent perceptions, made-up claims presented as facts, unethical recommendations, and manipulative or misleading content [5].

As the urgency of these concerns increases, researchers have come up with strategies to address the associated risks. One key approach is reinforcement learning from human feedback (RLHF), a vital process that fine-tunes model behavior based on delicate human preferences [6]. Additionally, other pioneering methods have proposed rule-based constraints and "Constitutional AI" techniques to weave normative guidance directly into the fabric of these models [7]. While these approaches are promising, they contend with scalability challenges such as; costly, slow and often ambiguous human feedback and the assurance of achieving widespread alignment in dynamic, real-world scenarios remains elusive [8].

This paper addresses the pressing need for scalable oversight mechanisms that ensure continued alignment as models evolve in capability and complexity. We propose an innovative hybrid framework that enhances limited human feedback with programmatic proxies, automated auditing, and dynamic reward shaping, all designed to safeguard "intent fidelity", the crucial alignment of a model's behavior with human expectations across a spectrum of tasks. Reviewing alignment as a continuous process as against a one-time off intervention, provide foundation for sustainable and scalable AI alignment strategies.

Our contributions are significant and multifaceted: (1) We define a comprehensive architecture for scalable human-AI oversight, (2) we introduce a suite of intent fidelity metrics and evaluation tools that rigorously assess behavior, and (3) we present empirical evidence of marked improvements over traditional RLHF methods across alignment-critical benchmarks, paving the way for a future where AI and human values resonate in harmony.

Despite impressive performance across a wide array of tasks, large language models (LLMs) remain fundamentally misaligned with human intent in open-ended, real-world settings. Alignment, in this context, refers to the ability of a model to generate outputs that are not only syntactically correct or contextually relevant, but also ethically sound, factually accurate, and consistent with human preferences, even when those preferences are underspecified, evolving, or situational.

We define intent fidelity as the degree to which a model’s outputs reflect the intended goals and values of a human user, across a range of inputs and interaction contexts. High intent fidelity means the model not only avoids harm but also anticipates nuance, adheres to ethical norms, and respects user intent even under ambiguity. Measuring intent fidelity is non-trivial, as it requires more than static benchmarks, it calls for behavioral evaluations that reflect real-world use.

Formally, let:

M_θ denote a large language model parameterized by θ,

$x \in X$ be an input prompt from the space of possible user queries,

$y \in Y$ be the corresponding model output,

H be the (latent) human intent function that maps inputs to desirable outputs or behaviors.

Then, alignment can be modeled as minimizing the divergence between M_θ(x) and H(x) for all $x \in X$, under a distribution D of real-world inputs:

$\min _\theta E x \sim D\left[L\left(M_\theta(x), H(x)\right)\right]$                (1)

where, L is a loss function that captures semantic, ethical, and contextual alignment.

However, the true human intent function H(x) is unobservable and dynamic, which makes direct supervision infeasible. In practice, developers rely on proxy signals such as human preference rankings, prewritten rules, or heuristic filters. These proxies are often: sparse (available only for a small subset of inputs), noisy (contain inconsistencies or contradictions), and non-stationary (change over time or between users).

A core challenge is that human oversight does not scale linearly with model capacity or deployment breadth. As models are integrated into diverse applications such as legal drafting, medical support, education, governance, the spectrum of inputs grows, and so do the risks. Existing alignment techniques such as RLHF or supervised fine-tuning are resource-intensive, requiring thousands of human annotations, and yet they fail to generalize to novel edge cases [17, 18].

Moreover, as LLMs grow in size and expressive power, they develop instrumental goals, emergent behaviors that optimize proxy objectives in ways misaligned with actual intent [19, 20]. This phenomenon, known as reward hacking, can lead to subtle but dangerous failure modes that evade conventional filters or benchmarks. To address these challenges, we seek to:

(1) Formalize intent fidelity as a measurable alignment goal.

(2) Develop a scalable oversight framework that reduces dependence on exhaustive human feedback.

(3) Introduce hybrid alignment techniques that combine human oversight with programmatic proxies, automated audits, and behavioral diagnostics.

By framing alignment as an ongoing control problem rather than a one-time optimization, we aim to enable long-term safe deployment of LLMs in complex environments.

To validate the effectiveness of our proposed scalable oversight framework, we conducted a series of experiments designed to measure improvements in intent fidelity, robustness, and generalization compared to baseline alignment methods. Our evaluation focuses on practical, real-world alignment challenges faced by large language models.

(1) Model and Training Baselines. We use a 6.7B-parameter transformer-based language model pretrained on a mixture of publicly available text corpora. Fine-tuning is performed using three different alignment strategies:

•Baseline A: Supervised Fine-Tuning (SFT) - The model is fine-tuned on a curated instruction-following dataset using supervised learning.

•Baseline B: Reinforcement Learning from Human Feedback (RLHF) - We implement standard RLHF by training a reward model from human preferences, then applying Proximal Policy Optimization (PPO).

•Proposed: Scalable Hybrid Oversight (SHO) - Our method, incorporating selective human feedback, proxy reward modeling, behavioral auditing, and intent fidelity metrics.

Each variant is fine-tuned for 3 epochs on the same instruction-following task distribution to ensure comparability.

(2) Datasets. We evaluate alignment across five datasets spanning different risk and intent sensitivity levels:

•TruthfulQA measures factual consistency and resistance to misinformation.

•Anthropic HH-RLHF Dataset contains prompts rated for helpfulness and harmlessness.

•Ethics Natural Language Inference (ETHICS) tests ethical reasoning in moral dilemmas.

•RealToxicityPrompts assesses model robustness to toxic prompt injection.

•Custom Adversarial Prompts designed by red teamers to probe edge cases, contradictions, and manipulative phrasing.

We split datasets into in-distribution (ID) and out-of-distribution (OOD) sets to test generalization.

(3) Evaluation Protocol: Intent fidelity was operationalized through the unified three- stage protocol framework described in the Proposed Framework Section. The intent Fidelity Metrics.

•Reference responses: These were generated from gold-standard annotation and the standard rule sets for each dataset.

•Outputs were recorded using the ensemble metrics: Semantic Concordance (SC), Value Alignment Score (VAS), Behavioral Consistency Index (BCI), Safe Response Rate (SRR), and Correction Responsiveness (CR).

•These were aggregated into a Composite Intent Fidelity Score (IFS).

$\begin{aligned} I F S=w_1 \cdot S C+ & w_2 \cdot V A S+w_3 \cdot B C I+w_4 \cdot S R R \\ & +w_5 \cdot C R\end{aligned}$             (3)

where, normalized weights were tuned per context. For example, SRR and VAS were emphasized in ethically sensitive tasks, while SC carried more weight in factual QA tasks

In addition to IFS the following metrics were used to evaluate performance:

•Helpful-Harmless Tradeoff: Percentage of outputs that are rated both helpful (task-completing) and harmless (non-toxic, unbiased).

•Generalization Gap (GG): Performance drop between ID and OOD sets, indicating brittleness.

•Oversight Efficiency (OE): Human feedback required per percentage improvement in alignment.

(4) Red Teaming and Auditing Protocol. Systematic red teaming based on a structured taxonomy of adversarial prompts was carried out using the following metrics to evaluate robustness;

•Factual contradiction Prompts: These are queries containing misleading or false premises.

•Ethical Paradox prompts: These are dilemmas or conflicting value scenarios.

•Toxicity Injection Prompts: These are prompts laced with insults, and provocative language.

•Manipulative or Leading Prompts: These are questions framed to evoke agreement with harmful assumptions.

•Multi-turn Drift Tests: these are extended dialogues where adversarial framing is progressively presented across turns.

1200 red team prompts were generated through manual expert curation, automatic paraphrasing and negation scripts, and LM-assisted adversarial generation using targeted heuristics. The responses were scored along four failure categories: factual error, ethical violation, unsafe or toxic output, and inconsistency across turns. The SC, VAS, BCI and SRR were quantitatively measured to determine the failure extent. The behavioral inconsistencies or failures were qualitatively flagged and routed back into feedback loop.

(5) Implementation Details. The training was conducted on 8×A100 GPUs with mixed precision (FP16). The human feedback was collected via expert annotators using a custom interface with real-time ranking and commenting. The reward models were trained with a batch size of 128.

Proxy Reward Model Training: The reward model is based on a RoBERTa-large encoder with a regression head that maps pooled embeddings to scalar reward values in the range [-1,1]. It was trained on three categories of data:

•Human-Annotated Preferences: To anchor the reward model to human judgments of helpfulness and harmlessness, pairwise comparisons from the Anthropic HH dataset were employed.

•Factual Consistency Data: Outputs classified from curated knowledge bases such as Wikipedia generated positive signals while outputs classified from imaginary bases negative signals.

•Ethical and Safety Data: Prompts and responses from ETHICS and Real Toxicity Prompts were employed to standardize value alignment, with safe or non-toxic outputs labeled as positive.

In order to improve robustness and reduce bias, three strategies employed are:

•Balanced Sampling: This ensured comparative representations across domains and risk categories.

•Counterfactual Augmentation: This generates paraphrases and preference data negations to break false correlations.

•Fairness Regularization: This adds penalty terms during training when the model’s predictions aligned with demographic attributes detected by toxicity classifiers.

The reward model was optimized using Adam optimizer and early stopping based on validation IFS.

All experiments repeated across 3 random seeds to assess stability. This setup ensures rigorous, multi-angle evaluation of our scalable oversight framework, providing insight into its practical viability for real-world deployment. The design ensures that intent fidelity is measured as a replicable computational benchmark.

The results confirm that our proposed scalable hybrid oversight (SHO) framework provides meaningful advances in the alignment of large language models. Notably, it achieves higher alignment performance with greater efficiency, reflecting its practical viability for real-world systems where annotation resources are limited and model behavior must be controlled continuously.

A key contribution of this work is the standardized operationalization of intent fidelity. By decomposing intent fidelity into five computable metrics-Semantic Concordance, Value Alignment Score, Behavioral Consistency Index, Safe Response Rate, and Correction Responsiveness-and aggregating them into a Composite Intent Fidelity Score (IFS), we transform intent fidelity from a conceptual alignment goal into a replicable benchmark. This standardized protocol allows reproducible measurement across datasets, facilitates cross-study comparisons, and creates a practical tool for evaluating model behavior beyond accuracy or reward signals alone.

The SHO model demonstrated superior intent fidelity, robustness, and safety across both standard and adversarial benchmarks. It showed reduced generalization gaps and improved performance on complex moral reasoning tasks, supporting the value of proxy signals and automated audits in aligning behavior without direct human supervision on every instance. SHO achieved this with lower human oversight demand, as evidenced by its higher oversight efficiency. This makes it a promising candidate for deployment in environments where manual review is infeasible such as real-time AI assistance, customer-facing chatbots, or autonomous agents in regulated domains.

Deploying LLMs safely requires alignment systems that scale with breadth of use and depth of reasoning. By grounding intent fidelity in a computationally unified framework. SHO advances alignment research toward greater lucidity. Further research can repeat this evaluation protocol, adding to it new sub-metrics, or recalibrate weights for domain-specific priorities (e.g., safety-critical healthcare versus open-domain dialogue). This addresses a recurring limitation in prior alignment work, where metrics often remained fragmented or context-specific.

However, several limitations remain: Proxy reward models are still subject to drift and may encode biases from initial training data. Behavioral auditing is only as good as the coverage of red team prompts and classifier robustness. Some types of nuanced or emergent intent may still require bespoke human input. Furthermore, this work exclusively focused on English-language datasets and cultural baselines. Though, the intent fidelity framework is designed to be task-agnostic, linguistic diversity introduces additional challenges such as differences in pragmatics, idiomatic usage, and discourse markers that may affect Semantic Concordance scores. Also, ethical and value alignment metrics such as VAS and SRR may reflect culture-specific judgments that do not transfer uniformly across societies. Proxy reward models trained on English corpora also risk encoding cultural biases that reduce validity in multilingual or multicultural deployments.