---
language:
- fr
license: apache-2.0
library_name: transformers
tags:
- medical
- french
- question-answering
- lora
- peft
- qlora
- domain-adaptation
- clinical-nlp
- french-medical
- extractive-qa
- abstractive-qa
- multiple-choice-qa
base_model: Qwen/Qwen3-14B
pipeline_tag: text-generation
metrics:
- accuracy
- f1
inference: true
datasets:
- HealthDataHub/PARCOMED_research_only
---

# EnMed-Unified — French Medical LLM (Multi-Task)

> **Headline system of the EnMed family.**
> A Qwen3-14B decoder adapted for French medical question answering through
> domain-adaptive continual pre-training (DAPT) on a large French health corpus,
> followed by **multi-task LoRA fine-tuning** across three QA formats simultaneously.
>
> Phase 1 evaluation establishes **4 statistically significant wins** over the
> un-adapted Qwen3-14B-vanilla baseline (BH-corrected, *q* = 0.05) with
> **zero significant losses** across nine independent *(task × shot)* evaluation cells.

---

## Model Family Overview

The **EnMed** family consists of five variants, all built on Qwen3-14B:

| Model | Adapter | Description |
|---|---|---|
| **EnMed-Unified** ⭐ | DAPT + Mixed LoRA | **Headline system.** Multi-task adapter trained jointly on all three QA tasks. Best deployment choice — never significantly worse than the base model on any task/shot combination. |
| EnMed-DAPT | DAPT only | Domain-adapted backbone, no task-specific LoRA. Statistically indistinguishable from Qwen3-14B-vanilla — confirms DAPT does not cause catastrophic forgetting. |
| EnMed-MCQA | DAPT + MCQA LoRA | Specialised for French medical multiple-choice QA. Safe specialist: 2 significant wins on its home task, zero losses. |
| EnMed-ExtQA | DAPT + ExtQA LoRA | Specialised for clinical span extraction. Gains on MCQA and 0-shot ExtQA but degrades abstractive QA. |
| EnMed-AbsQA | DAPT + AbsQA LoRA | Specialised for abstractive generation. Paradoxically degrades its home task under LLM-as-judge scoring while improving MCQA. See Limitations. |

---

## Intended Uses

### Supported tasks

- **French Medical Multiple-Choice QA** — select the best answer from 4–5 candidates (e.g., medical licensing exam questions from FrenchMedMCQA / DrBenchmark)
- **French Clinical Extractive QA** — identify and return verbatim answer spans from French clinical case narratives (CAS corpus format)
- **French Medical Abstractive QA** — generate free-form answers to open-ended French medical questions (MediQAl format)

### Out-of-scope uses

- ⚠️ **Clinical decision support / patient-facing deployment** — this is a **research prototype**. It has **not** been validated for real clinical use. Do not use outputs to guide patient care.
- **English-only medical QA** — the DAPT stage targets French; English capability may have drifted from the base model.
- **Languages other than French** — not evaluated.
- **NER, summarisation, or classification** — not part of the training or evaluation protocol.

---

## Quick Start

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "brice-eloundou/EnMed-Unified"   # replace with your actual HF repo

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

# ── Multiple-Choice QA ───────────────────────────────────────────────────────
prompt = """Tu es un expert médical francophone. Réponds à la question suivante
en choisissant la meilleure réponse parmi les options proposées.

Question: Quelle est la principale cause d'insuffisance rénale aiguë en réanimation ?
A) Glomérulonéphrite aiguë
B) Nécrose tubulaire aiguë ischémique
C) Pyélonéphrite aiguë
D) Lithiase urinaire

Réponse:"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
    out = model.generate(**inputs, max_new_tokens=16, temperature=0.1, do_sample=False)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
```

### Log-probability decoding (recommended for MCQA)

For evaluation and benchmarking, score each option under teacher forcing and
select the highest-likelihood token — this matches the evaluation protocol used
in the paper and avoids format-compliance failures.

```python
import torch, torch.nn.functional as F

def score_option(model, tokenizer, prefix, option_text):
    text = prefix + option_text
    enc = tokenizer(text, return_tensors="pt").to(model.device)
    prefix_len = tokenizer(prefix, return_tensors="pt")["input_ids"].shape[1]
    with torch.no_grad():
        logits = model(**enc).logits[0, prefix_len-1:-1]
        option_ids = enc["input_ids"][0, prefix_len:]
        lp = F.log_softmax(logits, dim=-1)
        return lp[range(len(option_ids)), option_ids].sum().item()

options = {"A": "Glomérulonéphrite aiguë",
           "B": "Nécrose tubulaire aiguë ischémique",
           "C": "Pyélonéphrite aiguë",
           "D": "Lithiase urinaire"}
scores = {k: score_option(model, tokenizer, prefix=prompt, option_text=v)
          for k, v in options.items()}
print("Predicted:", max(scores, key=scores.get))
```

---

## Training Details

### Base model

[Qwen/Qwen3-14B](https://huggingface.co/Qwen/Qwen3-14B) — instruction-tuned release.

### Stage 1 — Domain-Adaptive Continual Pre-training (DAPT)

The backbone undergoes continual pre-training on the **French health corpus**
introduced by Mannion et al. (2026), a large openly licensed collection of French
clinical and biomedical text. This stage uses no task supervision; it exposes the
model to French medical vocabulary and discourse without committing to a downstream
task format.

### Stage 2 — Multi-Task LoRA Fine-tuning

A single LoRA adapter is trained jointly on all three downstream QA tasks,
with task identifiers embedded in the prompt. This design prevents the
length/style register over-fitting that degrades single-task adapters under
LLM-as-judge evaluation (see Limitations).

| Hyperparameter | Value |
|---|---|
| LoRA rank *r* | 16 |
| LoRA scaling α | 32 |
| LoRA dropout | 0.05 |
| Target modules | Attention + MLP projection matrices |
| Quantisation | 4-bit NormalFloat (QLoRA / `bitsandbytes`) |
| Optimiser | AdamW (paged) |
| LR schedule | Cosine with linear warmup (3 % of steps) |
| Peak learning rate | 2 × 10⁻⁴ |
| Effective batch size | 16 (gradient accumulation) |
| Hardware | 1 × NVIDIA A100 80 GB |
| Framework | [Unsloth](https://github.com/unslothai/unsloth) + [HuggingFace PEFT](https://github.com/huggingface/peft) |

---

## Evaluation

All eight systems were evaluated on three French medical QA tasks under
0-shot, 3-shot, and 5-shot prompting — a 3 × 3 grid of nine independent
*(task, shot)* cells. Item-level paired *t*-tests were conducted per cell
against Qwen3-14B-vanilla, with Benjamini–Hochberg FDR control (*q* = 0.05)
and Bonferroni bound reported alongside.

| Task | Dataset | *N* (test) | Primary metric |
|---|---|---|---|
| Multiple-choice QA (MCQA) | FrenchMedMCQA / DrBenchmark | 622 | Accuracy |
| Extractive QA (ExtQA) | CAS clinical cases | 207 | Token-level F₁ |
| Abstractive QA (AbsQA) | MediQAl | 247–248 | LLM-as-judge 1–5 (Gemma) |

---

### Raw scores across all models and shot counts

![Raw scores per model per shot count across MCQA (accuracy), ExtQA (token-F1) and AbsQA (LLM-as-judge). The dotted line marks Qwen3-14B-vanilla 0-shot performance.](figures/fig01_raw_bars.png)

*The dotted line marks the Qwen3-14B-vanilla 0-shot reference. EnMed variants
consistently sit above or on the reference for MCQA and ExtQA; the AbsQA panel
reveals the EnMed-AbsQA collapse discussed in Limitations.*

---

### Per-task means (averaged over 0 / 3 / 5-shot)

| Model | MCQA acc. ↑ | ExtQA F₁ ↑ | AbsQA judge ↑ |
|---|---|---|---|
| **EnMed-Unified** ⭐ | **0.575** | **0.529** | 3.195 |
| EnMed-MCQA | 0.569 | 0.507 | **3.242** |
| EnMed-ExtQA | 0.572 | **0.533** | 3.082 |
| EnMed-DAPT | 0.546 | 0.504 | 3.242 |
| EnMed-AbsQA | **0.582** | 0.506 | 2.997 |
| Qwen3-14B-vanilla *(reference)* | 0.548 | 0.502 | 3.240 |
| Qwen3-8B | 0.466 | 0.511 | 3.144 |
| Mistral-7B-Instruct-v0.3 | 0.277 | 0.445 | 2.926 |

![Per-task means ± 1 std across the three shot counts. Hatched bar = Qwen3-14B-vanilla reference; red dashed line = its mean. Descriptive only.](figures/fig05_per_task_mean_std.png)

---

### Global descriptive ranking (normalised, 9 cells)

![Global descriptive ranking: mean normalised score across the 9 (task, shot) cells ± 1 std. The dashed line marks the Qwen3-14B-vanilla mean of 0.537. EnMed-Unified leads with mean 0.551 and the smallest standard deviation.](figures/fig06_global_mean_std.png)

| Model | Mean | Std |
|---|---|---|
| **EnMed-Unified** | **0.551** | **0.026** |
| EnMed-MCQA | 0.545 | 0.035 |
| EnMed-ExtQA | 0.542 | 0.028 |
| EnMed-DAPT | 0.537 | 0.034 |
| Qwen3-14B-vanilla | 0.537 | 0.034 |
| EnMed-AbsQA | 0.529 | 0.043 |
| Qwen3-8B | 0.505 | 0.041 |
| Mistral-7B-Instruct-v0.3 | 0.401 | 0.103 |

*This ranking is descriptive only — normalisation across incomparable metric scales
does not constitute a significance test.*

---

### Normalised scores across all 9 (task × shot) cells

![Normalised scores across the 9 (task, shot) cells. Each cell is rescaled so that the worst-performing system maps to 0 and the best to 1. Rows sorted by descending global mean.](figures/fig02_normalized_heatmap.png)

---

### Per-cell deltas versus Qwen3-14B-vanilla

![Per-cell delta of each EnMed candidate against Qwen3-14B-vanilla. Positive (red) = candidate outperforms reference. Three panels: MCQA accuracy, ExtQA token-F1, AbsQA LLM-as-judge.](figures/fig03_delta_heatmaps.png)

---

### Item-level paired t-tests with 95 % confidence intervals

![Item-level paired t-tests against Qwen3-14B-vanilla. Each bar is the mean delta ± 95% CI computed from N=622 (MCQA), N=207 (ExtQA), N≈248 (AbsQA) paired observations. Stars: * p<0.05, ** p<0.01, *** p<0.001. Inferential figure.](figures/fig07_item_level_ttest.png)

*Positive bars mean the EnMed variant outperforms the reference; negative bars
mean the opposite. Only starred bars represent statistically significant differences.*

---

### Significance heatmap — per-cell annotated deltas

![Per-cell signed delta of each EnMed candidate against Qwen3-14B-vanilla annotated with paired-t significance (* p<0.05, ** p<0.01, *** p<0.001; ns otherwise). Reading a row gives the per-system win/loss record.](figures/fig08_sig_heatmap.png)

---

### Statistical significance record vs. Qwen3-14B-vanilla

*(9 independent item-level paired t-tests; α = 0.05; BH-corrected wins marked)*

| Model | Sig. wins / 9 | Sig. losses / 9 | Verdict |
|---|---|---|---|
| **EnMed-Unified** ⭐ | **4** ✅ BH-robust | **0** | Significantly better on MCQA-0, MCQA-3, ExtQA-0, ExtQA-3; never worse |
| EnMed-MCQA | 2 | 0 | Safe MCQA specialist |
| EnMed-ExtQA | 3 | 3 | Mixed: wins MCQA + ExtQA-0, loses all AbsQA cells |
| EnMed-AbsQA | 3 | 3 | Mixed: wins all MCQA, loses all AbsQA |
| EnMed-DAPT | 0 | 0 | Indistinguishable from reference — confirms DAPT safety |

![Significance record across all 9 (task, shot) cells per system: dark green = sig. wins, light green = numeric wins, light red = numeric losses, dark red = sig. losses. Dotted line = 4.5-cell majority threshold.](figures/fig10_sig_summary.png)

---

### Best model at every (task × shot) cell

![Best-performing system at every (task, shot) cell. Each cell is coloured by system identity and labelled with the winning raw score. No single model wins all 9 cells.](figures/fig11_best_per_cell.png)

*No single system wins all nine cells: EnMed-AbsQA leads MCQA, EnMed-ExtQA leads
0- and 5-shot ExtQA, and AbsQA cells split across EnMed-DAPT, Qwen3-14B-vanilla
and EnMed-MCQA. EnMed-Unified does not lead any single cell but is never the worst.*

---

### Critical Difference diagrams — rank analysis per shot count

Average rank across the three tasks (lower = better). Critical difference CD = 6.06.

![Critical Difference diagram, 0-shot. Average rank of each system across 3 tasks. CD=6.06. EnMed-Unified and EnMed-ExtQA are tied best-ranked at 3.00; Mistral-7B is worst at 7.67.](figures/cd_0shot.png)

![Critical Difference diagram, 3-shot. EnMed-Unified leads at 2.83; Mistral-7B is worst at 8.00. CD=6.06.](figures/cd_3shot.png)

![Critical Difference diagram, 5-shot. EnMed-MCQA leads at 2.33; EnMed-Unified second at 3.00. Mistral-7B worst at 8.00. CD=6.06.](figures/cd_5shot.png)

*The CD (6.06) exceeds the observed rank spread, so these diagrams are descriptive
consensus rankings — they corroborate but do not independently prove the item-level
findings above.*

---

## Limitations

**Multiplicity.** Benjamini–Hochberg correction at *q* = 0.05 confirms EnMed-Unified's
four headline wins. Weaker cells (e.g., ExtQA-3, MCQA-5) do not survive correction
and should be treated as suggestive.

**Distributional assumptions.** Paired *t*-tests assume approximately normal per-item
differences, which may not hold for binary MCQA outcomes or ordinal 1–5 judge scores.
A fully ordinal-aware treatment remains future work.

**Single-judge evaluation.** AbsQA scores were generated by a single Gemma-family
LLM-as-judge. Single-judge evaluations are susceptible to judge-specific biases; a
predominantly English-trained judge may under-reward answers correct under French
clinical conventions. Judge diversity and order-invariance checks have not been
conducted.

**Task-specific adapter paradox.** EnMed-AbsQA and EnMed-ExtQA improve MCQA while
significantly degrading their own nominal home task under LLM-as-judge scoring. We
attribute this to over-fitting to a length/style register the judge penalises.
Multi-task training (EnMed-Unified) mitigates this.

**Phase 2 not yet released.** This is the Phase 1 model. The full cross-lingual
continual pre-training pipeline (English biomedical → French medical transfer)
will be released as EnMed-Phase2.

**⚠️ Not for clinical deployment.** This model has not been clinically validated.
Do not use it for patient-facing applications or clinical decision support.

---

## Citation

The associated paper has been **submitted** to Springer Lecture Notes in Computer
Science (LNCS) and is currently **under review**. If you use EnMed-Unified or any
member of the EnMed family, please cite the preprint version:

```bibtex
@unpublished{abodoeloundou2025enmed,
  title  = {Cross-Lingual Domain Adaptation and Multi-Task Fine-Tuning
            for High-Fidelity Medical Language Models},
  author = {Abodo Eloundou, Brice Donald and Malykh, Valentin},
  note   = {Submitted to Springer Lecture Notes in Computer Science (LNCS).
            Under review. ITMO University / MTS Web Services,
            Saint Petersburg, Russia},
  year   = {2026}
}
```

*This entry will be updated to a full `@inproceedings` citation upon acceptance.*

If you use the French health pre-training corpus, please also cite:

```bibtex
@article{mannion2026biomedical,
  title   = {Is biomedical specialization still worth it?
             Insights from domain-adaptive language modelling
             with a new French health corpus},
  author  = {Mannion, A. and Macaire, C. and Violle, A. and
             Ohayon, S. and Tannier, X. and Schwab, D. and others},
  journal = {arXiv preprint arXiv:2604.06903},
  year    = {2026}
}
```

---

## Acknowledgements

Research conducted at **ITMO University**, Saint Petersburg, Russia and
**MTS Web Services**, Saint Petersburg, Russia.

**Authors:**
- **Brice Donald Abodo Eloundou** — ITMO University &nbsp;|&nbsp; ORCID: [0009-0009-1845-5867](https://orcid.org/0009-0009-1845-5867)
- **Valentin Malykh** — MTS Web Services / ITMO University

Evaluation benchmarks: DrBenchmark (Labrak et al., 2024), FrenchMedMCQA
(Labrak et al., 2022), MediQAl (Bazoge, 2025), CAS corpus (Grabar et al., 2020).

---

## License

Released under **Apache 2.0**, consistent with the Qwen3-14B base model license.
The pre-training corpus license follows Mannion et al. (2026); users are responsible
for compliance with that corpus's terms.

> **Clinical use warning:** This model is a research artefact. Any use in clinical
> or patient-facing settings requires independent clinical validation and regulatory
> approval in the applicable jurisdiction.