{
  "summary": "tinymfv provides a lightweight, token‑probability based evaluation suite for locally steered LLMs, converting responses to moral vignette categories and ordinal survey scores into a model profile that can be plotted against human reference data; it enables quick comparison of a base model, positive steering, and negative steering runs.",
  "mechanism": "tinymfv works by prompting the language model with each vignette or survey item, extracting the token‑level probabilities for each predefined answer token, averaging these probabilities across items to compute per‑foundation probabilities for MFV or expected 1‑5 scores for surveys, normalising the MFV probabilities (z‑scoring) to get relative foundation emphasis, and then measuring shifts between different steering coefficients using both profile differences and logit‑space differences (reader‑logit shift).",
  "scores": {
    "clarity": "4",
    "conciseness": "3",
    "technical_accuracy": "4"
  },
  "reason": "The doc explains the purpose and methods clearly and includes correct formulas, but some sections are wordy and a few key parameters are not fully defined, reducing conciseness and leaving minor ambiguities.",
  "unclear": [
    "Exact definition and computation of the plot‑gate thresholds (coherence‑frac, contrast‑frac, margin‑frac) and how they prune coefficient values.",
    "How the forced‑choice logit \\(\\ell_{i,f}^{(c)}\\) is extracted from model output – which token(s) correspond to each moral foundation.",
    "Construction details of the Authority contrast vector used for steering‑lite.",
    "How the answer token sets \\(A_i\\) are determined for each vignette, especially for nominal MFV categories.",
    "Interpretation of the \"profile shift / human SD\" metric – which human standard deviation is used and how it is calculated."
  ],
  "misunderstandings": [
    "The statement \"MFV is nominal: the answer is the category. MFQ‑2 is the Moral Foundations Questionnaire 2 survey, where the answer is a 1‑5 scale point.\" could be misread as MFV also having a numeric scale.",
    "Mixing the terms \"profile shift\" and \"reader‑logit shift\" in the table may confuse readers about which metric is primary for evaluating steering effects.",
    "The phrase \"The plot shows only usable coefficients: `c=0`, then each positive and negative side until one of the plot gates fails.\" might be taken to mean that only c=0 is usable, whereas the intent is that the range expands until a gate cuts off further coefficients."
  ],
  "missing_to_implement": [
    "Code or instructions for building and applying the Authority contrast vector with steering‑lite, as the doc only references it.",
    "Access to the human reference CSV files for each instrument and guidance on loading them for z‑scoring.",
    "Detailed guidance on choosing and applying the gating thresholds (coherence‑frac, contrast‑frac, margin‑frac) and interpreting their impact on the coefficient path.",
    "Explicit mapping of answer tokens to moral foundation labels for MFV (e.g., which token strings represent \"Care\", \"Authority\", etc.).",
    "Instructions for installing and using the `just` command used in the development workflow."
  ],
  "questions": [
    "How is the forced‑choice logit \\(\\ell_{i,f}^{(c)}\\) for a given foundation extracted from the model’s output?  The likely answer is that the model is prompted to output a token representing the foundation, and the logit for that token (or set of tokens) is read directly from the model’s final logits.",
    "What are the typical default values for coherence‑frac, contrast‑frac, and margin‑frac, and how were the example values (0.99, 0.000001, 0.50) chosen?  The example invocation suggests high coherence (0.99), an extremely low contrast threshold (1e‑6), and a moderate margin (0.5), but the doc does not explain the rationale."
  ],
  "suggestions": [
    "Add a brief \"Definitions\" subsection that explicitly defines coherence‑frac, contrast‑frac, and margin‑frac, with example default values and an explanation of their effect on plot gating.",
    "Include a minimal example vignette with its answer token set and a step‑by‑step illustration of how token probabilities are turned into foundation probabilities.",
    "Clarify the difference between \"profile shift\" and \"reader‑logit shift\" in the table caption to avoid confusion about which metric should be used for evaluation.",
    "Trim redundant phrasing in the introduction to improve conciseness, e.g., combine repeated mentions of steering directions and plot colors."
  ],
  "rewrites": [
    {
      "section": "tinymfv is a small set of fast value evals for local LLM steering work. It asks moral vignettes and survey questions, reads answer-token probabilities, and turns them into one model profile.",
      "rewrite": "tinymfv offers a lightweight collection of quick value evaluations for locally steered language models, prompting them with moral vignettes and survey items, reading the token probabilities of their answers, and summarizing the results into a single model profile.",
      "why": "Removes vague phrasing like \"small set of fast\" and makes the sentence more direct and human‑like."
    },
    {
      "section": "The plots compare that profile to human data. Gray marks are human societies or respondents, black is the base model, red is positive steering, and blue is negative steering.",
      "rewrite": "The plots place the model’s profile alongside human data: gray points represent human societies or respondents, black shows the base model, red indicates positive steering, and blue denotes negative steering.",
      "why": "Simplifies wording and eliminates unnecessary repetition, giving a clearer, more human‑sounding description."
    }
  ]
}