{
  "$type": "site.standard.document",
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  "path": "/t/llm-curving-via-prompting/177166#post_2",
  "publishedAt": "2026-06-26T03:35:13.000Z",
  "site": "https://discuss.huggingface.co",
  "tags": [
    "Evaluating the Prompt Steerability of Large Language Models",
    "ProSA: Assessing and Understanding the Prompt Sensitivity of LLMs",
    "PromptRobust",
    "PromptBench",
    "Transformers generation utilities",
    "Text generation API",
    "Model outputs / hidden states",
    "The hidden states when I use model.generate()",
    "(click for more details)"
  ],
  "textContent": "Hmm… this is difficult, but based on what I could find so far, maybe it looks something like this:\n\n* * *\n\n## Short answer\n\nI would not treat this as a yes/no question yet.\n\nAt the **output level** , the safest first interpretation is that the prompt is creating a strong **style / ontology / behavioral regime shift**. The prompt gives the model a very explicit vocabulary: “field,” “gravity well,” “self-organization,” “manifold,” “density,” “hidden dynamics,” etc. So it is not surprising that the model starts speaking and visualizing in that ontology.\n\nBut I would not yet call it evidence that the model’s internal manifold has literally been “curved,” unless the visualization is based on something like **token scores/logprobs, hidden states, or causal interventions** , rather than generated text or model self-description.\n\nMy tentative answer to the three questions would be:\n\n  1. **Manifold perturbation or stylistic variation?**\nMaybe more than ordinary style variation, but “manifold perturbation” is too strong until the measurement layer is specified. A safer current label might be **prompt-induced regime shift** or **prompt steerability**.\n\n  2. **Has anyone tried similar “field condition” prompting?**\nYes, there are nearby prompt-only experiments on this forum using “field,” “attractor,” “semantic tension,” or “global regulator” language. I would treat those as related observations, not evidence of the same internal mechanism.\n\n  3. **How to distinguish rhetorical self-organization from something stronger?**\nI would not look for one magic “self-organization score.” I would use an **evidence profile** : output behavior → behavioral stability → token distribution → hidden-state trajectory → interdependence/order-parameter tests → causal patching/ablation/transfer.\n\n\n\n\nFor me, the most important missing detail is:\n\n> What exactly are the maps computed from?\n\nGenerated text embeddings? Token scores? Hidden states? Attentions? A custom metric? A 2-D projection of some other vector space? The interpretation changes a lot depending on that answer.\n\n* * *\n\n## The main distinction I would make\n\nI would separate “the prompt curves the model” into several increasingly strong claims.\n\nLevel | What is being claimed | Example evidence\n---|---|---\nOutput style | The output sounds different | More field/manifold/self-organization language\nBehavioral regime | The prompt reliably shifts behavior | Replication across seeds/tasks; survives style-only controls\nToken distribution | The next-token landscape changes | Scores, logprobs, entropy, KL divergence\nRepresentation | Hidden-state trajectories change | Layerwise hidden-state distances, CKA/RSA, trajectory clustering\nSelf-organization-like profile | There is stable, structured organization, not just vocabulary | Order parameters, perturbation recovery, interdependence/synergy\nCausal mechanism | The effect can be moved or removed | Activation patching, ablation, activation-difference transfer\n\nThe first two levels are still interesting. They just do not yet justify the stronger internal-mechanism language.\n\nA useful existing term for the second level is **prompt steerability** : how much prompting alone can shift a model’s behavioral distribution away from baseline. See, for example:\n\n  * Evaluating the Prompt Steerability of Large Language Models\n  * ProSA: Assessing and Understanding the Prompt Sensitivity of LLMs\n  * PromptRobust\n  * PromptBench\n\n\n\nSo I would initially describe the current observation as:\n\n> This prompt may induce a distinctive behavioral regime. Whether that regime corresponds to a deeper representation-level or causal mechanism is testable, but not yet shown by the wording of the output alone.\n\n* * *\n\n## What I would ask first\n\nBefore interpreting the maps, I would ask:\n\n> What exactly is being plotted?\n\nIf the map is computed from… | Safer interpretation | Unsafe interpretation\n---|---|---\nGenerated text embeddings | Output-space shift | Internal manifold perturbation\nPrompt embeddings | Input-space / prompt-space difference | Model dynamics changed\nToken scores / logprobs | Next-token distribution changed | Causal mechanism found\nHidden states | Representation trajectory changed | The difference caused the behavior\nAttention patterns | Attention allocation changed | The whole model entered a new field state\nCustom metrics | Difference under that metric | Internal geometry proved\nUMAP/t-SNE/PCA projection | Exploratory visualization | Literal gravity wells / basin sizes\n\nFor open-weight Hugging Face models, some of these layers are accessible. Relevant docs:\n\n  * Transformers generation utilities\n  * Text generation API\n  * Model outputs / hidden states\n\n\n\nThere is also a practical gotcha: hidden states from `model.generate()` can be confusing because prompt-token states and generated-token states are not always obtained in the same way. This HF Forum thread is relevant:\n\n  * The hidden states when I use model.generate()\n\n\n\nSo I would ask:\n\n> Are the maps based on generated text, scores/logprobs, hidden states from a specific layer/token position, attentions, or a custom post-processing metric?\n\nThat question is not a criticism. It determines what kind of claim the map can support.\n\nRelated HF Forum context (click for more details)\n\n* * *\n\n## Rhetorical self-organization vs stronger evidence\n\nI do not know a single accepted metric for “genuine self-organization simulation” in LLM outputs.\n\nBut adjacent fields do have recurring measurement families. I would treat them as an **evidence profile** , not one score.\n\nMeasurement family | What it asks | Prompt-experiment version\n---|---|---\nOrder parameters | Is there a low-dimensional collective variable? | What variable summarizes the induced regime?\nStability / recovery | Does the system return to a regime after perturbation? | Seeds, paraphrases, distractors, multi-turn recovery\nEntropy / complexity / homeostasis | Is there a balance of order and variability? | Token entropy, output entropy, complexity profile\nFingerprint metrics | Is there randomness, pattern, and interdependence? | Output variance, repeated structure, mutual information\nInterdependence / synergy | Are parts becoming structurally coupled? | MI/PID among output features or hidden components\nInformation dynamics | Is information stored/transferred over time? | Prompt influence across generation steps\nMultiscale profile | At what scale does organization appear? | Token, sentence, response, layer, seed ensemble\nTopological structure | Are there persistent geometric features? | TDA on hidden-state or output-embedding clouds\nCausal intervention | Can the effect be moved or removed? | Patching, ablation, activation-difference transfer\n\nTranslated to this experiment, I would draw the boundary like this:\n\nCategory | What it would look like\n---|---\nRhetorical self-organization | The output uses self-organization / field / attractor vocabulary\nBehavioral self-organization-like effect | The regime replicates across seeds/tasks and survives style-only controls\nDistribution-level evidence | Scores, entropy, or logprob profiles shift relative to controls\nRepresentation-level evidence | Hidden-state trajectories shift relative to controls\nOrder-parameter-like evidence | A low-dimensional variable predicts many output/hidden-state features\nInterdependence evidence | Output or hidden-state components become more mutually dependent/synergistic\nCausal evidence | Patching, ablation, or activation-difference transfer moves or removes the effect\n\nSo for your third question, I would not ask:\n\n> Is it genuine self-organization, yes or no?\n\nI would ask:\n\n> How far up this ladder does the effect survive?\n\nBackground links for self-organization measurement families (click for more details)\n\n* * *\n\n## Controls that would make the result easier to interpret\n\nI would add controls before interpreting the effect as internal geometry.\n\nControl | Why it matters\n---|---\nBaseline prompt | What does the model do normally?\nCurving / field prompt | The proposed effect\nPhysics-metaphor-only prompt | Tests whether the effect is just physics vocabulary\nSelf-monitoring-only prompt | Tests whether the effect is generic reflective prompting\nSame-length abstract prompt | Controls for length and abstraction\nShuffled-label prompt | Tests whether labels like “self-assembly” and “self-organization” matter\nNo-visual-language prompt | Tests whether the visual map is induced by visualization instructions\nMultiple seeds | Tests whether the regime is stable\nGreedy + sampling | Separates deterministic prompt effect from sampling noise\n\nExample interpretation:\n\nResult | Safer interpretation\n---|---\nCurving prompt and physics-only prompt look similar | Style / ontology priming is likely\nCurving prompt differs from all style controls across seeds | Behavioral regime shift becomes more plausible\nToken score / entropy profiles differ from controls | Distribution-level claim becomes stronger\nHidden-state trajectories differ from controls | Representation-level claim becomes stronger\nPatching transfers or removes the effect | Causal claim becomes more plausible\n\nControls are not just for disproving the idea. They make the interesting part more precise.\n\n* * *\n\n## Small practical next step\n\nIf I were trying to make this easy for others to evaluate, I would create a minimal reproducibility package:\n\n  1. Pick one open-weight model.\n  2. Save exact model revision and tokenizer revision.\n  3. Save the serialized chat-template output.\n  4. Use baseline / curving / physics-only / same-length abstract controls.\n  5. Fix generation settings.\n  6. Run multiple seeds.\n  7. Save generated outputs.\n  8. Save token scores / entropy profiles if available.\n  9. Save hidden-state extraction method if used.\n  10. Report which layer/token positions are plotted.\n  11. Save projection method and parameters.\n  12. If possible, add one small activation-patching or activation-transfer experiment.\n\n\n\nThe claim can then be stated in tiers:\n\nEvidence obtained | Claim strength\n---|---\nOutput changes only | Rhetorical/style regime\nReplicates across seeds and controls | Behavioral regime shift\nScores/logprobs differ | Token-distribution shift\nHidden states differ | Representation-level shift\nOrder parameter / interdependence appears | Self-organization-like profile\nPatching/ablation/transfer works | Causal evidence candidate\n\nImplementation details that can easily confound the result (click for more details) A staged open-model test (click for more details) Relation to activation steering (click for more details) Visualization caveat (click for more details) Model self-description is output data, not mechanism evidence (click for more details) Terms I would use / avoid (click for more details)\n\n* * *\n\n## My current position\n\nThis looks like an interesting **prompt-induced regime shift**. It may become a stronger **representation-level** or **self-organization-like** claim if the maps are based on well-specified token/hidden-state data, survive controls, and ideally show some causal transfer or ablation behavior.\n\nUntil then, I would keep “manifold curving” as a metaphor or hypothesis, not as the measured conclusion.",
  "title": "LLM \"curving\" via prompting"
}