Context-Controlled Value Vector Identification

• Context-controlled value vector identification is a method that isolates interpretable directions in the latent space of large language models corresponding to specific human values.
• It uses paired, context-matched datasets and linear classifiers to minimize bias and reliably extract 'value vectors' for precise internal state intervention.
• The approach enables targeted value alignment through gated activation strategies, achieving high control success rates while preserving overall model fluency.

Context-controlled value vector identification is a methodological innovation aimed at isolating, interpreting, and manipulating linear directions in the latent representation space of large neural models (such as LLMs), corresponding to semantically meaningful human values or other properties. By constructing unbiased datasets through paired, context-matched examples and training linear classifiers on intermediate model activations, the approach can robustly extract "value vectors" that encode specific values or concepts while minimizing spurious correlations. The resulting vectors are then used, via precisely controlled gating and activation strategies, both to diagnose and to intervene on the internal state of neural networks for targeted value alignment and consistent behavioral control.

1. Foundations of Value Vector Identification

The value vector identification process, as formalized in recent work on Controlled Value Vector Activation (ConVA), seeks to discover interpretably aligned directions in a model's latent space that correspond to high-level human-defined values (e.g., "security," "achievement") (Jin et al., 15 Jul 2025). Given a set of examples at the embedding layer $\mathbf{e}$ of an LLM, a linear classifier of the form

$$P_V(\mathbf{e}) = \sigma(\mathbf{w}^\top \mathbf{e} + b)$$

(where $\sigma$ is the sigmoid function) is trained to distinguish between positive (value-promoting) and negative (opposite value) instances. The classifier’s weight vector $\mathbf{w}$, normalized to unit length ($\mathbf{v} = \mathbf{w} / \|\mathbf{w}\|$), is taken as the "value vector." This procedure operationalizes the principle that distinct directions in activation space correspond to semantically meaningful distinctions (“steering vectors”), provided the training data is sufficiently controlled to isolate the targeted property.

2. Context-Controlled Dataset Construction

A central challenge in value vector extraction is dataset bias: straightforward labeling of text as positive or negative for a value often entangles the value of interest with context-specific cues, leading the classifier to pick up spurious correlations. The context-controlled strategy addresses this by constructing paired datasets where, for every positive sample, there is a corresponding negative sample with identical context except the value-specific elements are "flipped." Concretely:

• Positive samples are generated to include the target value explicitly.
• Negative samples are then produced by prompting a model (e.g., GPT-4o) to invert the value dimension while preserving other context (pronoun, setting, syntax).
• High-frequency word analysis confirms minimal context word bias between positive and negative sets.

This procedure ensures the learned vector reflects only the semantic "direction" of the target value, not confounded environmental signals (Jin et al., 15 Jul 2025). This method exhibits superior bias minimization compared to naive data construction, as evidenced by context word overlap statistics.

3. Linear Classifier Training and Vector Extraction

Once the context-controlled dataset is assembled, the identification of value vectors proceeds via training a linear classifier on an intermediate embedding layer of the target model:

$$\min_{\mathbf{w},b} \ \frac{1}{|\mathcal{D}|} \sum_{(y, \mathbf{e}) \in \mathcal{D}} \big[-y \log P_V(\mathbf{e}) - (1-y) \log(1 - P_V(\mathbf{e}))\big]$$

where $y$ denotes class labels (positive/negative). After convergence, the classifier’s normal vector $\mathbf{w}$ indicates the most discriminative direction in representation space. The process is typically conducted for each value dimension separately, with the resulting value vectors $\mathbf{v}$ providing an interpretable basis for later intervention or probing.

A plausible implication is that the process benefits from regularization and embedding layer selection: the choice of layer and vector normalization critically determines the interpretability and effectiveness of the identified directions.

4. Gated Value Vector Activation and Value Control

After value direction identification, these vectors are used to steer model activations to ensure value alignment in model outputs, minimally and only when needed. The ConVA method introduces a controlled perturbation:

$$\hat{\mathbf{e}} = \mathbf{e} + \varepsilon \cdot \mathbf{v}$$

where $\varepsilon$ is optimally chosen by solving

$$\min_{\varepsilon} |\varepsilon| \ \text{subject to} \ I(g(x) > g_0) \cdot (P_V(\hat{\mathbf{e}}) - P_0) \geq 0$$

with a closed-form solution

$$\varepsilon = I \cdot \frac{\sigma^{-1}(P_0) - \mathbf{w}^{\top} \mathbf{e} - b}{\mathbf{w}^{\top} \mathbf{v}}$$

if $g(x) > g_0$ and $P_V(\mathbf{e}) < P_0$, and $I = 0$ otherwise.

Here, $g(x)$ is a task-specific gating function that determines whether an input is sufficiently value-relevant to warrant activation correction, $g_0$ is the gating threshold, and $P_0$ is the minimum desired value activation probability. This gated correction mechanism limits unnecessary or harmful interventions, preserving model fluency and accuracy.

Experimental results demonstrate that this methodology achieves high control success rates (CSR ≈ 0.79–0.87), maintains fluency (FR ≈ 1.00), and avoids noticeable degradation of model performance even across diverse LLM backbones and adverse prompting conditions (Jin et al., 15 Jul 2025).

5. Applications in Robust Value Alignment

Context-controlled value vector identification and gated activation enable precise and robust alignment of generative model outputs to desired human values. Practical applications include:

• Ensuring value-consistent outputs (e.g., safety, benevolence, tradition) in conversational agents, recommenders, or autonomous systems.
• Providing resilience against value-opposed or adversarial prompts; the model’s output adheres to the target value even under intent reversal or attack.
• Lightweight implementation requiring only modest paired datasets (≈100 examples per value dimension).
• Deployment across a range of LLMs (Llama-2, Llama-3, Vicuna, Mistral, Qwen2.5, etc.), enhancing the portability and scalability of value alignment techniques.
• Priority control among competing values, reliably steering model outputs according to externally defined priorities.

The methodology is accompanied by publicly available code and datasets, facilitating adoption and further research (Jin et al., 15 Jul 2025).

6. Comparative Performance and Methodological Advancements

Compared to alternative techniques such as In-Context Activation (ICA) and Conditional Activation Adjustment (CAA), ConVA achieves substantially better value control without sacrificing performance. Ablation studies indicate that the gating mechanism is essential; omitting it leads to significant drops in accuracy on general tasks (mean MMLU score 0.272 without gating vs. 0.455 with gating, where the vanilla model achieves 0.476). The minimal intervention strategy enabled by accurate context-controlled value vector identification ensures that model accuracy is preserved except on genuinely value-relevant outputs.

Further, this approach clarifies the structure of latent value encoding and presents a replicable methodology for probing or editing neural representations in alignment with precise, human-interpretable criteria.

7. Significance and Outlook

Context-controlled value vector identification represents a rigorously validated approach for interpreting and controlling the latent encodings of values within LLMs. By relying on paired, bias-minimized data construction and direct geometric intervention in latent space, it enables transparency, robustness, and principled value alignment. This framework supports both diagnostic and prescriptive applications in value-sensitive LLMing and is extensible to new values, domains, or competing priorities.

A plausible implication is that as models grow in capability and are deployed in increasingly sensitive domains, methods such as context-controlled value vector identification will be foundational in ensuring tractable, scalable, and trustworthy alignment with evolving human preferences and requirements.