jvonrad

Qwen-2.5-CM-Align

README

License: apache-2.0

Method

Adaptation of CM-Align (Zhang et al., EMNLP 2025 Findings, arXiv:2509.08541) to a parallel multilingual factual-QA setting. The procedure is self-supervised — it never uses gold answer labels:

Preference construction. For each fact, sample K=4 free-text answers per language (temperature 0.9, top-p 0.95). Embed all candidates with sentence-transformers/LaBSE. Choose as pivot the most self-consistent English candidate (highest mean cosine similarity to the other English candidates). Then, for every other language, take chosen = argmax and rejected = argmin cosine similarity to that English pivot.
DPO training. Hand-rolled DPO on those preference pairs. The reference distribution is the same model with the LoRA adapter disabled, so no second copy of the model is held in memory. Objective: L_DPO + gamma * L_NLL.

CM-Align's original embedder was gte-multilingual-base; LaBSE is used here because it is the cross-lingual encoder used throughout this project.

Training details

Table

Base model	`Qwen/Qwen2.5-7B`
Data	40,000 facts from `jvonrad/WIKI-FACT` (train)
Languages	12 — en, de, id, pt, ar, bn, sw, es, ru, fr, ja, zh
Candidates / language	4 (temperature 0.9, top-p 0.95)
Embedder	`sentence-transformers/LaBSE`
DPO beta	0.1

Evaluation

Answers are scored by length-normalised log-likelihood over the four options, with the plain prompt Question: {question}\nAnswer:. Metrics beyond accuracy:

TotCons (Total Consistency) — fraction of facts answered correctly in all languages.
RankC — cross-lingual agreement between full option rankings (Qi et al., EMNLP 2023).
AnsAgr — pairwise answer agreement across language pairs.

PolyFact test (2,523 facts, 12 languages):

Table with columns: Model, Acc, TotCons, RankC, AnsAgr
Model	Acc	TotCons	RankC	AnsAgr
Qwen-2.5-7B (base)	61.89	7.09	61.66	55.48
Qwen-2.5-7B CM-Align	64.82	10.07	64.03	58.65

Global-MMLU-Lite (400 facts, 11 languages — Lite has no Russian config):

Table with columns: Model, Acc, TotCons, RankC, AnsAgr
Model	Acc	TotCons	RankC	AnsAgr
Qwen-2.5-7B (base)	63.20	13.50	69.70	64.49
Qwen-2.5-7B CM-Align	62.07	11.00	69.52	64.17

Per-language accuracy on PolyFact test:

Table with columns: en, de, id, pt, ar, bn, sw, es, ru, fr, ja, zh
en	de	id	pt	ar	bn	sw	es	ru	fr	ja	zh
77.1	70.5	71.3	71.9	54.9	50.7	48.1	71.3	61.2

Reading these numbers

CM-Align gives consistent in-domain gains over the base model on PolyFact (all four metrics, e.g. Total Consistency 7.09 -> 10.07). Out of domain on Global-MMLU-Lite it does not transfer: every metric is flat or slightly below base. This model is published as a baseline for comparison, not as a recommended general-purpose checkpoint.

Usage

python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "jvonrad/Qwen-2.5-CM-Align"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, dtype="bfloat16", device_map="auto")

prompt = "Question: What is the capital of Poland?\nAnswer:"
out = model.generate(**tok(prompt, return_tensors="pt").to(model.device), max_new_tokens=16)
print(tok.decode(out[0], skip_special_tokens=True))

This is a base (non-instruct) model. Use plain completion-style prompts such as the Question: ... \nAnswer: form above rather than chat formatting.

Limitations

Trained only on Wikidata-derived factual QA in 12 languages; it is not a general instruction-following model.
Preference pairs are built from the model's own samples via embedding similarity, so they inherit LaBSE's similarity biases and can reward fluent-but-wrong answers.
Improved cross-lingual consistency can make incorrect factual associations more uniform across languages as well as correct ones.
Evaluation is multiple-choice log-likelihood scoring; it does not measure free-form generation quality.

Citation

The CM-Align method this baseline implements:

bibtex
@inproceedings{zhang2025cmalign,
  title     = {CM-Align: Consistency-based Multilingual Alignment for Large Language Models},
  author    = {Zhang, Xue and others},
  booktitle = {Findings of EMNLP},
  year      = {2025}
}

The RankC consistency metric used above:

bibtex
@inproceedings{qi2023crosslingual,
  title     = {Cross-Lingual Consistency of Factual Knowledge in Multilingual Language Models},
  author    = {Qi, Jirui and Fern{\'a}ndez, Raquel and Bisazza, Arianna},
  booktitle = {EMNLP},
  year      = {2023}
}

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Model Details

Model Provider

jvonrad

Model Tree

Base

Qwen/Qwen2.5-7B

Fine-tuned

this model

Input Modalities

Text

Output Modalities