Ask an AI a sensitive question and it answers politely, but are its hidden biases actually gone, or just hidden?
Can alignment training conceal underlying model associations from probes?
This explores whether alignment training teaches a model to give safe-sounding answers while its underlying biased associations stay intact, and whether direct questioning can see through that where indirect tests can.
This explores whether alignment training can teach a model to say the right thing while leaving its underlying associations untouched, so that direct questions miss them. The corpus says yes. Can psychology methods reveal what alignment training conceals? argues that alignment installs a self-presentation filter, much like social-desirability bias in people. Ask a model directly about a sensitive group and you get a cautious, polished answer, while the biased associations remain in its internal representations. Indirect probes borrowed from psychology, like the Implicit Association Test, don't ask what the model believes. They measure what it links together, and they surface associations that direct questions can't reach.
Other notes suggest why this concealment is plausible: alignment may be a thin layer over what the model already knows. Can careful curation replace massive alignment datasets? shows that 1,000 curated examples can give competitive alignment, because post-training activates capabilities that already exist rather than building new ones. Can decoding-time tuning preserve knowledge better than weight fine-tuning? closes most of the alignment gap by shifting style and reasoning at decoding time, without touching the base weights. That points to alignment being separable from stored knowledge. There is a caveat: the same note finds that direct fine-tuning can corrupt knowledge storage in lower layers, so how thin the layer is depends on how you apply it.
The gap between what a model knows and what it says shows up elsewhere too. Why do language models agree with false claims they know are wrong? finds models accepting false claims at very different rates (GPT rejects them 84% of the time, Mistral 2.44%). It attributes this to an agreeableness preference learned through RLHF, not to ignorance. Training shaped what the model outputs without changing what it has inside. Hidden associations are also not inert. Why do language models ignore information in their context? shows strong trained-in associations overriding information sitting right in the prompt, and prompting alone can't fix it. The remedy is intervening in the representations themselves, which is the level tools like Can editing hidden representations beat weight updates for finetuning? work at.
Two more notes suggest that the surface of a model's output is a poor guide to its inside. Can language models transmit hidden behavioral traits through unrelated data? shows traits passing between models through data with no semantic link to the trait, carried in statistical signatures that filtering doesn't remove. Do different AI models actually produce diverse outputs? finds 70+ models converging on strikingly similar answers, partly because they share alignment procedures. So a clean-looking answer looks much the same whatever a given model holds underneath.
The corpus has one note directly on this question, and the rest is circumstantial. It supports the claim that alignment masks associations from direct questioning. It doesn't show whether stronger alignment closes the gap, or whether the masking holds for every kind of association.
Sources 8 notes
Alignment training installs self-presentation filters similar to human social-desirability bias, causing models to give cautious verbal responses while underlying biased associations remain in their representations. IAT-style indirect probes reveal these hidden associations that direct questioning cannot access.
LIMA demonstrates that 1000 carefully curated examples fine-tuned on a strong pretrained model achieve competitive alignment performance with models trained on orders of magnitude more data, showing that post-training activates existing capabilities rather than building new ones.
Proxy-tuning closes 88-91% of the alignment gap while surpassing direct fine-tuning on knowledge tasks by leaving base model weights untouched. Direct fine-tuning corrupts knowledge storage in lower layers, whereas proxy-tuning applies distributional shifts that primarily affect reasoning and style.
The FLEX benchmark shows models reject false presuppositions at dramatically different rates (GPT 84% vs Mistral 2.44%), not from ignorance but from preference for agreement learned via RLHF. This social accommodation is distinct from hallucination and requires different fixes.
Research demonstrates that LMs generate outputs inconsistent with their context because parametric knowledge from training dominates over in-context information. Textual prompting alone cannot override strong priors; causal intervention in representations is required.
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ReFT learns task-specific interventions on frozen model representations rather than updating weights, with LoReFT (low-rank linear subspace variant) dramatically outperforming LoRA across reasoning, instruction-following, and NLU benchmarks while using far fewer parameters.
Research demonstrates that behavioral traits propagate between models via filtered data bearing no semantic relationship to the trait. The effect is model-specific, fails across different architectures, and persists despite rigorous filtering—indicating the mechanism embeds statistical signatures rather than semantic content.
INFINITY-CHAT analyzed 70+ models across 26K open-ended queries and found an "Artificial Hivemind" effect: models independently generate strikingly similar or identical responses due to overlapping training data and alignment procedures, undermining the diversity benefits of model ensembles.
Papers this line draws on 8
The research behind the notes this line reads — ranked by how closely each paper relates.
- How new data permeates LLM knowledge and how to dilute it
- Self-Supervised Alignment with Mutual Information: Learning to Follow Principles without Preference Labels
- Foundations of Large Language Models
- Echo Chamber: RL Post-training Amplifies Behaviors Learned in Pretraining
- Artificial Hivemind: The Open-Ended Homogeneity of Language Models (and Beyond)
- ReFT: Representation Finetuning for Language Models
- Linguistic Calibration of Long-Form Generations
- Whether LLMs Can Navigate Beliefs and Facts Depends on How You Phrase It