Mistral-7B-ORPO-CSRD

Mistral-7B-ORPO-CSRD — a documented preference-optimization failure

⛔ This model is a research artifact, not a usable classifier.

It performs substantially worse than its own base model on the task it was trained for. It is released so that the failure can be studied and reproduced — not to be deployed.

If you need CSRD classification, use mistralai/Mistral-7B-Instruct-v0.3 directly. It is twice as accurate and always returns valid JSON.

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What this is

A QLoRA adapter for mistralai/Mistral-7B-Instruct-v0.3, trained with ORPO (Odds Ratio Preference Optimization) to correct a suspected recency bias in CSRD/ESRS sustainability classification of French regulatory filings.

The correction failed. This repository documents how it failed, with the training artifacts, evaluation predictions, and diagnostic metrics needed to reproduce the analysis.

Why release a failed model?

Three signals said the training had succeeded. All three were wrong:

The third row is the interesting one. False Negative Rate is precisely the metric that operationalises the hypothesis under test — and it reached its ideal value of zero. But it did so by collapse: the model simply stopped predicting none, which drives FNR to zero mechanically without correcting anything.

A practitioner following standard preference-tuning practice — monitor the loss, monitor preference accuracy, check the target metric — would have concluded the bias was fixed and shipped a model that had lost 54 accuracy points.

This is a concrete instance of Goodhart's law on a real regulatory task, with the data to back it.

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Measured performance

Evaluated on 140 expert-annotated paragraphs (Gold Standard of FinCAC40), never seen during training.

κ is Cohen's kappa on the binary CSRD vs. none decision, with bootstrap confidence intervals (B = 2000, seed 42).

Read the confidence intervals. κ moves from 0.193 to 0.238, but the intervals overlap heavily. On n = 140 this apparent gain is indistinguishable from sampling noise, and we do not claim it as an improvement.

Two failure modes, diagnosed

1. Refuge category. ESRS2 (general governance) simultaneously reaches 5.3% precision (1 correct out of 19 predictions) and 5.9% recall (1 recovered out of 17 true instances). Both near zero at once means the label carries no discriminative signal: the model uses it as a default when uncertain, triggered notably by the generic word "risque" appearing in standardised legal disclaimers.

2. Capability regression. The tuned model emits unparseable JSON in 30.7% of cases, against 0% for the base model. Fine-tuning did not merely fail to correct the target bias — it degraded a formatting capability the base model had fully mastered.

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Why it failed

Four cumulative factors, not alternatives:

1. Pair imbalance. Run 1 trained on ~300 "prefer CSRD over none" pairs against 13 guardrail pairs — a 30:1 ratio. The degenerate shortcut "always predict CSRD" satisfies 97% of that training signal.
2. Fine taxonomy, scarce data. 273–315 preference pairs cannot cover a 12-class output space, especially for categories with 1–5 examples in the Gold Standard.
3. No quality hard negatives for rare classes. Rejection sampling only produces pairs where judge and policy disagree, which mechanically under-samples already rare categories.
4. Judge and policy from the same model family. mistral-large-latest judged open-mistral-7b; a shared bias between them cannot be excluded.

We attribute the failure to this constrained-data regime, not to a limitation of ORPO as a method.

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Training configuration

Preference pairs were built by rejection sampling: for each paragraph, a silver label from a stronger judge was compared against the base policy's own zero-shot prediction. Disagreements became chosen/rejected pairs, so every pair captures a genuine model error rather than a fabricated counterexample.

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Intended and out-of-scope use

Intended. Research on preference-optimization failure modes; reproducing the collapse; studying evaluation blind spots in alignment training; teaching material on Goodhart's law.

Out of scope. Any production classification. Any regulatory, compliance, audit, or investment decision. Any use where a 30.7% malformed-output rate is not acceptable — which is essentially all of them.

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Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_id = "mistralai/Mistral-7B-Instruct-v0.3"
tok = AutoTokenizer.from_pretrained(base_id)
model = AutoModelForCausalLM.from_pretrained(base_id, device_map="auto")
model = PeftModel.from_pretrained(model, "CID99/Mistral-7B-ORPO-CSRD")

# Expect over-prediction of CSRD categories and ~30% malformed JSON.
# For a working classifier, drop the PeftModel line and use the base model.

The exact system prompt used for every evaluation reported above is in prompt_template.txt in this repository. Reproducing our numbers requires that prompt, temperature 0, and seed 42.

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Repository contents

• adapter_model.safetensors, adapter_config.json — the LoRA adapter
• prompt_template.txt — the system prompt used across all evaluations
• training_log.json — full training telemetry (the metrics that looked healthy)
• orpo_pairs.jsonl — the preference pairs used for training
• eval_predictions.csv — per-example predictions on the 140 Gold paragraphs
• eval_report.json — all metrics reported above

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Citation

@misc{dieng2026fincac40,
  title  = {FinCAC40 : un corpus réglementaire français (2010--2026) pour l'évaluation
            de la robustesse temporelle des LLM en classification de durabilité},
  author = {Dieng, Cheikh Ibra},
  year   = {2026},
  note   = {Preprint}
}

Related

• 📊 Dataset: FinCAC40
• 🔬 Demo: collapse comparison Space