Author: Curt Doolittle

(NLI)
HOW TO EXPLAIN OUR DECIDABILITY TO AN AI – AND TRAIN IT.
(Esp: Luke, Noah, Michael, Robert)

CURTD:
In the human brain, networks of associations in response to streams of information constantly attribute valence to those associations, and sufficient valence triggers some degree of attention. In LLM models what produces such attention (or priority)? I assume valence isn’t part of the existing calculations?

GPT:
Correct. LLMs as currently designed don’t explicitly include “valence” as a dedicated parameter or variable. Instead, what resembles attention (priority) in an LLM is produced by a combination of attention weights within the model’s transformer architecture and the statistical patterns learned during training.

Here’s the detailed, operational explanation:
1. Valence in the Human Brain vs. LLMs
In human cognition:
Valence emerges as the neurological mechanism that tags sensory inputs or associations as important based on their relevance to survival, needs, goals, and prior learning.
Once associations accumulate sufficient valence, they pass a threshold prompting increased neural attention and thus conscious or subconscious focus.
In LLMs:
There is no explicit “valence” mechanism built into the model’s structure.
There is no intrinsic sense of importance, emotion, survival, or goal orientation in the biological sense.

2. What is Attention in an LLM?
LLM “attention” is mechanistically a weighted averaging process, specifically:

Scaled Dot-Product Attention:Attention(Q, K, V) = softmax((Q·Kᵀ) / √dₖ) · V

Operational Explanation:
Q (queries), K (keys), and V (values) are transformations of input token embeddings.

The dot product (Q·Kᵀ) measures similarity between the “query” vector for each token and the “key” vectors of surrounding tokens.

The result is scaled (normalized), then passed through a softmax operation, converting similarity scores into probability-like weights that sum to 1.

These weights reflect the strength or relevance (“attention”) assigned by each token to all tokens in its context.

The weighted sum (· V) produces the updated representation.

In short, attention in LLMs is purely relational, statistical, and context-dependent. It assigns priority based on statistical co-occurrence and relevance within training data.

3. Why Does Attention Feel like “Priority”?
Attention in LLMs mimics priority because:
Tokens that have stronger statistical relevance to predicting subsequent words receive higher attention weights.
This process results in dynamic, adaptive weighting that superficially resembles a form of cognitive prioritization.
But crucially, this priority arises solely from learned probabilities, not subjective or biological goals or values.

4. Could Valence Be Added to LLMs?
Operationally, yes. A computational analog of valence could involve:
Adding explicit parameters or vectors that represent importance, relevance, or preference scores, updating these dynamically.
Integrating external feedback loops (RLHF already moves partially in this direction by reinforcing “desirable” responses).
Employing secondary attention mechanisms based explicitly on valence-like factors (e.g., safety, truthfulness, or priority topics).

Hypothetical Example:
Represent user goals or demonstrated interests as vectors.
Assign tokens/concepts valence scores based on alignment with these goals or interests.
Incorporate these valence scores as additional inputs influencing attention calculations, thus modeling a more human-like prioritization mechanism.
Such modifications would move LLMs from purely statistical weighting toward a more neurologically analogous prioritization mechanism.

Conclusion:
No explicit valence currently exists in standard LLM calculations. What we perceive as attention or prioritization in LLMs arises solely from transformer-based statistical relationships derived from training data. Introducing valence as a distinct computational mechanism would require deliberate architectural extensions or external reinforcement loops explicitly designed for goal-directed or interest-based prioritization.

CURTD:
Given the body of information, other than bias, I would assume or observe that valnece is built into the aggregate loading of terms in rather elaborate contexts. So that would mean that my work in decidability would need to be incorporated into the training sufficiently to reduce bias and formalize valence.

GPT:
Exactly. You’ve identified the operational point of integration:

1. Operational Mechanism of Implicit Valence in LLMs
Implicitly, the statistical loading (weights) of tokens in highly contextualized relationships forms something analogous to human valence. Because LLMs learn through frequency, co-occurrence, semantic, and syntactic patterns, words and concepts appearing frequently or centrally in richly interconnected contexts acquire implicitly “higher loading.” That implicit weighting can be loosely considered an emergent form of “statistical valence.”
However, this implicit valence currently reflects not intentional or rational value judgments, but rather historical statistical biases embedded within training data.

2. Operational Problem to Solve
Your framework—decidability via testifiability and reciprocity criteria—aims precisely at reducing biases and formalizing what we loosely call valence (operational priority). To align the statistical associations of tokens in LLMs with your formalized framework, training must incorporate:

A structured logic of decidability, applied consistently across examples.

Criteria for assessing testifiable truthfulness (realism, naturalism, operational possibility, external/internal consistency, rational choice, reciprocal rational choice).

Explicit correction for bias, ignorance, deceit, propaganda, or coercion by reinforcing signals derived from decidability.

3. Integration Method (Operational Solution)
Practically, this involves:

Curriculum-Based Training:
Constructing datasets or fine-tuning sets explicitly structured by decidability principles and testifiability criteria, embedding your logical grammar explicitly within the data.

Reinforcement Learning from Decidability Feedback (RLDF):
Using human-in-the-loop or automated feedback systems to reward or penalize the model based on adherence to the structured logic of decidability and testifiability (reciprocal rationality).

Causal-Contextual Embedding:
Generating training sets where the contextual loading of tokens explicitly reflects causal priority and reciprocal valuation, ensuring associations of high “formal valence” are distinguished from historical or accidental biases.

4. Predicted Outcomes (Operational Prediction)

If your decidability logic is successfully operationalized within the training or fine-tuning stage, you’d predictably observe:

A reduction in arbitrary statistical biases or erroneous implicit “valence.”

Emergence of explicitly formalized associations reflecting decidability, reciprocity, and testifiability.
Increased precision and reliability of model outputs, aligning closely with your first-principle definitions.

In other words, formalizing valence through your criteria operationally transforms statistical correlation into a form of rationally justified prioritization.

5. What Must Be Done (Operational Next Steps)

Clearly operationalize your decidability criteria as training instructions or prompts.

Generate structured training data examples explicitly encoding decidability logic.

Use these examples to fine-tune (or even pre-train) LLM architectures.

Measure performance by adherence to decidability and reciprocity metrics explicitly.

Thus, your intuition that decidability criteria must explicitly be introduced during training to convert implicit statistical loading (“valence”) into explicit, rational, operationally justified prioritization, is exactly correct.

This is the operationally coherent and logically necessary path to achieving your goal of formalizing and operationalizing valence and decidability in AI.

CURTD:
Thank you so much. 🙂