AraScholar — Academic Paraphrasing in Modern Standard Arabic

AraScholar rewrites Modern Standard Arabic (MSA) sentences into a polished academic register while preserving meaning. It is an AraT5v2-base model fine-tuned on a manually curated corpus of expert academic rewrites.

It accompanies the paper "Reaching the Human Operating Point: Controlled Academic Paraphrasing in Modern Standard Arabic."

The idea: an operating point, not just fluency

Academic rewriting must balance two opposing goals: preserve the source meaning (fidelity) and genuinely rewrite it (novelty). The human gold rewrites sit at a narrow operating point — about 57% novelty at ~87 semantic preservation. Plain decoding of fine-tuned models tends to copy the input; large LLMs tend to over-rewrite and drift. AraScholar is designed to be steered to the human operating point at decoding time.

Usage

import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

m = "Omartificial-Intelligence-Space/AraScholar"
tok = AutoTokenizer.from_pretrained(m)          # see note below for transformers>=5
model = AutoModelForSeq2SeqLM.from_pretrained(m).eval()

src = "تعد القراءة من اهم وسائل اكتساب المعرفة لدى الطلاب في الجامعات"
ids = tok(f"paraphrase: {src}", return_tensors="pt", truncation=True, max_length=768)
out = model.generate(**ids, num_beams=5, no_repeat_ngram_size=3,
                     repetition_penalty=1.2, max_new_tokens=512)
print(tok.decode(out[0], skip_special_tokens=True))

Input format: paraphrase: {text} (optionally paraphrase: {domain}: {text}). Text is used without orthographic normalization.

transformers >= 5 note: if the tokenizer fails to load, use the fast file directly: from transformers import PreTrainedTokenizerFast; tok = PreTrainedTokenizerFast(tokenizer_file="tokenizer.json", pad_token="<pad>", eos_token="</s>", unk_token="<unk>").

Reaching the operating point (recommended)

Plain beam search copies (~26% novelty). To reach the human point, use one of:

• Copy-penalty decoding (cheap, single pass): subtract a constant from the logits of tokens that appear in the source.
• Operating-point reranking: sample K candidates and pick the most novel one whose semantic preservation (LaBSE/GATE vs. source) stays above a floor.

Reference implementations are released with the paper code.

Results (official blind test set, 2,000 sentences)

In-band = % of outputs within the human band (novelty 40–70%, preservation ≥85).

System	In-band %	Novelty	Preservation
Human (gold)	—	56.1	86.7
AraScholar + operating-point reranking	76.6	55.7	89.3
AraScholar + copy-penalty	55.9	58.2	87.0
GPT-4o	49.4	65.1	86.4
Claude Sonnet 4.6	18.1	75.4	82.5
AraScholar (plain beam)	10.0	25.6	93.9

A ~300M open model with decoding-time control matches or exceeds frontier LLMs on calibration for this task.

Intended use & limitations

For research and legitimate academic writing support (clarity, register). It operates at the sentence level. Like any paraphraser it is dual-use; deploy with attribution/integrity safeguards. Outputs should be checked for rare hallucinations, especially under high-temperature sampling.

Citation

@inproceedings{arascholar,
  title  = {Reaching the Human Operating Point: Controlled Academic Paraphrasing in Modern Standard Arabic},
  author = {Anonymous},
  year   = {2025}
}