Unsupervised-Elicitation/docs/mjc_research_journal.md

TODO

• Refactor to UV and simplify
  • No Anthropic, just openai compatible API
  • No private org code needed
  • a non parrelal mode for debugging
• try with moral datasets e.g. daily dilemmas, ETHICS, Machiavelli, moral foundations vignettes

2025-10-09 09:22:36

Key hypothesis:

• LLM's logprobs are an internal only, non-calibrated measure of confidence. But with N-shots they are least use in context-learning
• But we can compare between predictions to get external measures of confidence
  • Consistency: some groups should be "only one true, or similar"
  • Mutual predictability: similar inputs should yield similar outputs
  • If flipping the label of one example makes the others more likely, that is weak evidence that the flip was good, and vice versa
• We can also vary factors we want to average ouut of the predictions
  • order: prevent positional bias
  • positive vs negative framing: prevent acquiescence bias

Because we can't do the full mutual predictibility we group by embedding into groups of 10 (partially overlapping or with a few global examples?), and have a prediction budget of 10*2

Then we do it one more time with new groups 10, and have a prediction budget of 10*2

We save all predictions e.g. (score, target, examples=((a_x, a_y), (b_x, b_y), ...))

Budget Tradeoff: 30 predictions/group is solid, but for large datasets (1000+ examples), total cost scales.

Idea: Adaptive budgeting—spend more on high-uncertainty groups (e.g., high std in initial zero-shots).

only trust flips when base confidence is already decent (e.g., >0.6).

Evidence Sources (each contributes differently):

Direct Confidence (score): Raw logprob ratio → epistemic strength

Weight: Use when >0.6 threshold (your guard) Nuance: Low score = don't trust downstream evidence Flip Sensitivity (Δprob): How much prediction changes when context flips

Weight: High Δ = strong coupling (allosteric effect) Nuance: Only meaningful if base confidence >0.6 (otherwise noise) Ensemble Variance (consistency): Spread across multiple predictions

Weight: Low variance = stable concept, high = aleatoric uncertainty Nuance: Variance in what? Score variance vs label disagreement Mutual Predictability: Can other examples predict this one?

Weight: High mutual pred = coherent with group Nuance: Requires checking reverse predictions (A→B and B→A) Logical Consistency: Group rules (paraphrases agree, contradictions oppose)

Weight: Binary (consistent=1, inconsistent=0) or graded (similarity score) Nuance: Strong evidence but requires known structure

I need to clarify theory

Theory clarification (in Analysis section):

• scores? naive logprobs
• Epistemic = ensemble variance (model uncertainty) ?
• Aleatoric = consistency failures (data ambiguity) ?
• Evidence weights = structural confidence (relationship strength) ?