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REVIEW 3 major objections 8 minor 47 references

Exploring Subjective Tasks in Farsi: A Survey Analysis and Evaluation of Language Models

T0 review · 3 major / 8 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read Farsi is de facto low-resource for subjective NLP despite its 127 million speakers, with only 15 public datasets and highly unstable LLM performance.

desk verdict A useful first survey and evaluation of Farsi subjective NLP, but the scarcity claim leans on a search method that could undercount datasets and the experiments lack variance reporting. read the letter →

arxiv 2509.05719 v1 pith:KX4LECXK submitted 2025-09-06 cs.CL

Donya Rooein , Flor Miriam Plaza-del-Arco , Debora Nozza , Dirk Hovy This is my paper · ORCID
classification cs.CL
keywords FarsiNLPsubjectivetaskssentimentanalysisemotiontoxicitydetectionlow-resourcelanguagesdemographicmetadataLLMevaluation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Farsi has more than 127 million speakers and a large Wikipedia presence, which usually earns it a "middle-resource" label in NLP. This paper argues that the label hides a deeper problem: for subjective tasks—emotion analysis, sentiment analysis, and toxicity detection—Farsi is effectively low-resource. Reviewing 110 publications, the authors find only 15 publicly available datasets, most lacking demographic metadata such as age and gender. In their benchmark, zero-shot LLMs perform poorly and unstably, especially on emotion, while a fine-tuned multilingual encoder model consistently outperforms them. The paper concludes that raw text volume is not enough to lift a language's prospects in subjective NLP.

What carries the argument

The paper's argument runs through two instruments. First, a survey inventory that adapts a prior annotation framework to code each of 110 papers by task, dataset availability, source, size, labels, modality, and presence of demographic metadata. Second, a comparative benchmark: three open-source LLMs (Llama-3-8B, Mixtral-8x7B, Qwen2-7B) in zero-shot prompting with two templates, plus XLM-RoBERTa fine-tuned separately on each of nine datasets, with a Farsi-to-English translation condition. The macro-averaged F1 scores across this task-by-dataset-by-model matrix carry the instability claim, and the fine-tuning comparison carries the finding that fine-tuning consistently improves performance.

What would settle it

Search beyond the top-10 Google Scholar results and outside ACL Anthology—local Iranian conferences, institutional repositories, and non-indexed venues—for public Farsi emotion, sentiment, or toxicity datasets; if those searches turn up many additional public datasets with demographic metadata, the scarcity claim weakens. Alternatively, rerun the benchmark on a broader stratified sample of Farsi texts and observe whether LLM F1 scores remain stable across datasets; stability would contradict the paper's 'highly unstable' claim.

Watch

Extended reading notes

Core claim

Resource status should be judged task by task, not by corpus size. Farsi's abundant web text and Wikipedia do not yield usable supervised data for emotion analysis, sentiment analysis, or toxicity detection. A structured review of 110 publications surfaces only 15 public datasets (7 EA, 5 SA, 3 TD), many from the same narrow sources (Twitter/Instagram and Digikala reviews); only two include demographic information, and few document annotator agreement. In evaluation, LLMs show highly unstable macro-averaged F1 scores: emotion near 0.2–0.4, sentiment 0.42–0.68, toxicity 0.56–0.94 depending on dataset and model. Fine-tuning XLM-RoBERTa improves every task, while translating Farsi to English gi

Load-bearing premise

The survey's completeness rests on restricting the search largely to ACL Anthology and the top 10 Google Scholar results per keyword; if many public Farsi subjective-task datasets sit outside those channels, the scarcity claim would be overstated, and the evaluation also assumes the nine selected datasets are representative of Farsi subjective tasks, a limitation the authors acknowledge.

Editorial extensions

If this is right

  • Language-resource classifications should become task-specific: a language can be text-rich yet supervision-poor for subjective tasks.
  • Translation to English is not a reliable remedy for low-resource subjective NLP, since it did not consistently improve LLM scores.
  • Fine-tuned encoder-only models are a stronger default than zero-shot open LLMs on all three Farsi tasks in this study.
  • New Farsi subjective dataset creation should prioritize demographic metadata and annotator documentation, not just dataset size.
  • Evaluations of Farsi subjective NLP should report per-dataset results, because averages hide large swings across datasets.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Inference: LLM 'stability' claims for low-resource languages should be treated as dataset-specific until shown otherwise; model rankings from one Farsi dataset are unlikely to transfer.
  • Inference: The poor emotion-analysis results may stem partly from label-scheme mismatch, since the datasets do not align with standard emotion frameworks; cross-lingual emotion benchmarks may be hard to interpret even with more data.
  • Inference: The dominance of social-media and e-commerce sources suggests that datasets from other registers, such as news, literature, or spoken language, could shift both task difficulty and model rankings.
  • Inference: A testable extension would be to collect demographic-aware Farsi annotations and check whether age- and gender-conditioned evaluation reveals systematic bias that current datasets cannot expose.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 8 minor

Summary. The paper surveys 110 publications on three subjective NLP tasks in Farsi (sentiment analysis, emotion analysis, toxicity detection), classifying them by dataset creation, public availability, annotation framework, modality, source, and demographic metadata. It reports 15 publicly available Farsi datasets across the three tasks and evaluates three open-source decoder-only LLMs (Llama-3-8B, Mixtral-8x7B, Qwen2-7B) in a zero-shot setting, together with fine-tuned XLM-RoBERTa, on nine datasets, including an experiment on translating data to English and comparing two prompt templates. The main conclusions are that Farsi subjective NLP suffers from scarce public datasets and missing demographic/annotation documentation; that LLMs perform poorly and unstably across datasets and models, especially on emotion analysis; and that fine-tuning consistently improves performance.

Significance. If the survey inventory is reliable, the paper provides a useful and much-needed resource map for Farsi subjective NLP, identifying specific gaps in emotion analysis, toxicity detection, and demographic metadata. The experimental benchmark is a reasonable first comparison of open encoder and decoder models on these tasks, and the authors make the list of reviewed papers publicly available, which is a reproducibility asset. The paper also credibly shows that translation to English does not consistently help and that prompt variation has limited impact on emotion analysis. However, the central survey claim depends on a search completeness assumption that is not validated, and the experimental conclusions are not supported by statistical estimates of variability.

major comments (3)
  1. [Section 3] The paper's central claim of a 'lack of publicly available datasets' (Abstract) rests on a literature search restricted to ACL Anthology and the top-10 Google Scholar results per query via SerpApi. No snowballing from references, no recall check against existing Farsi surveys, and no queries to Persian-language repositories (e.g., SID, Magiran, Ensani) are reported. Because the count of 15 public datasets (Table 1) is the load-bearing evidence for the survey's main conclusion, the possibility of undercounting is a substantive correctness risk. Please add a validation/recall check against known Farsi surveys (e.g., Rajabi & Valavi 2021; Asgarnezhad & Monadjemi 2021; Borowczyk 2023), report coverage statistics, or soften the scarcity claim accordingly.
  2. [Section 5.2 / Table 4] Table 4 reports single macro-F1 values without error bars, confidence intervals, or significance tests. The abstract's 'highly unstable across datasets and models' is a claim about variability, but the paper provides no variance estimates, no repeated runs, and no information about sampling temperature or seeds for the decoder-only models. It is therefore impossible to distinguish genuine model/dataset instability from evaluation noise. Please report repeated-run statistics, standard deviations, and ideally significance tests for the main comparisons (e.g., fine-tuned XLM-R vs. zero-shot LLMs).
  3. [Section 5.1 / Section 4.2.1] Prompt template selection was performed on subsamples of EmoPars and MirasOpinion, and the same datasets are then used again in the final evaluation reported in Table 4. If the prompt-selection subsamples were not disjoint from the final evaluation subsets, the reported advantage of template (II) may be inflated by test-set tuning. Please state whether the selection data were held out, or use a separate development set for prompt selection.
minor comments (8)
  1. [Table 1] Typographical issues: 'disguss' should be 'disgust'; the 'Farsi.Task' run-in is a formatting error; the EmoPars labels 'E - [disgust] + [hatred]' do not match the prose description that mentions 'wonder' (or surprise) instead of disgust. Please align the table with the dataset's actual label set.
  2. [Section 3.2] The sentence 'Only authors of three datasets (Yazdani and Shekofteh, 2022) provide detailed documentation...' is grammatically garbled and does not identify which three datasets are meant. Please rewrite and name the datasets.
  3. [Throughout] Inconsistent terminology: 'Mixtral-7B' vs. 'Mixtral-8x7B', 'SentiPars' vs. 'SentiPers', and 'NLBB' should be 'NLLB' (No Language Left Behind). Please standardize.
  4. [Appendix B.3] The heading 'Model hyperparameters' has no content under it; the hyperparameters appear in prose later. Either move the prose under the heading or remove the empty heading.
  5. [References] Llama 3 is cited twice as 'Dubey et al., 2024' and 'Grattafiori et al., 2024' with the same title and same arXiv identifier (arXiv:2407.21783). These appear to be duplicate references; please merge.
  6. [Table 3] The header 'T emplate A vg. F1 (I) (II)' is confusing. It should clearly indicate separate columns for Template (I) and Template (II) F1 scores.
  7. [Section 6] The conclusion 'fine-tuning consistently improves performance across all tasks' is supported only at the task-average level in Table 4; on Pars-OFF, Qwen2-7B (0.925) exceeds XLM-RoBERTa (0.854). Please qualify the claim.
  8. [Section 7] The Limitations section acknowledges biases in evaluation datasets but does not mention the survey search's reliance on top-10 Google Scholar results or the absence of Persian-language database queries. This is an important limitation of the main survey claim and should be stated.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the survey and experiments are empirical, self-contained observations with no derivation that reduces to its own inputs.

full rationale

This is an empirical survey and evaluation, not a derivation, and I find no step in which an output is equivalent to an input by construction. The dataset inventory (15 public datasets, Section 3.2) is compiled from ACL Anthology and Google Scholar/SerpApi searches; the claim of scarcity is an inductive summary of that inventory, not an assumption whose truth is presupposed. The only direct self-citation is the adoption of the annotation framework of Plaza-del Arco et al. (2024) in Section 3.1; that framework supplies generic metadata categories (annotation framework, language, modality, source, size) and is not used to define the paper's conclusions, so it is not load-bearing. Prompt template (II) is chosen after comparing two templates on a subsample (Section 5.1) and then used in Section 5.2; this is tuning on the same data, which could be a methodological limitation, but the reported F1 scores are observations, not predictions forced by the choice, and the instability claim does not reduce to the prompt selection. The NLBB translation choice is a manual model selection (Section 4.2.2), not a fitted parameter renamed as a prediction. The limitation acknowledged in Section 7 (reliance on existing public datasets) concerns coverage and generalizability, not circularity. The only serious risk is survey completeness due to the top-10 SerpApi cap, which is a correctness/coverage concern outside the circularity definition. Score 0.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

The paper is empirical and introduces no fitted constants. Its assumptions are about search coverage, dataset representativeness, and evaluation protocol. The only free design choice reported as a selection is the prompt template.

free parameters (1)
  • Prompt template selection = Template II
    Selected after evaluating two templates on subsamples of EmoPars and MirasOpinion; this choice is tuned on the evaluation data and could inflate reported F1 scores.
assumptions (4)
  • domain assumption ACL Anthology plus top-10 Google Scholar results per query is a complete or representative sample of Farsi subjective-task research.
    Underlies the survey's conclusion that only 15 public datasets exist. Section 3 describes the search limitation.
  • domain assumption The nine datasets selected for evaluation are representative of Farsi subjective tasks.
    Generalizes the benchmark results; authors acknowledge this is a limitation in Section 7.
  • domain assumption Manual annotation of the 110 reviewed papers using the extended framework is reliable and consistent.
    Survey counts depend on this annotation (Section 3.1).
  • domain assumption Zero-shot prompting with a single template is a fair measure of LLM capability for these tasks.
    All LLM results use one prompt template (II) chosen after a small pilot; Section 4.2.1.

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Cite this review

Pith. "Pith review of Exploring Subjective Tasks in Farsi: A Survey Analysis and Evaluation of Language Models." pith.science (2026). https://pith.science/paper/KX4LECXK

@misc{pith2026250905719,
  author       = {Pith},
  title        = {Pith review of: Exploring Subjective Tasks in Farsi: A Survey Analysis and Evaluation of Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KX4LECXK}},
  note         = {Machine review of arXiv:2509.05719}
}
read the original abstract

Given Farsi's speaker base of over 127 million people and the growing availability of digital text, including more than 1.3 million articles on Wikipedia, it is considered a middle-resource language. However, this label quickly crumbles when the situation is examined more closely. We focus on three subjective tasks (Sentiment Analysis, Emotion Analysis, and Toxicity Detection) and find significant challenges in data availability and quality, despite the overall increase in data availability. We review 110 publications on subjective tasks in Farsi and observe a lack of publicly available datasets. Furthermore, existing datasets often lack essential demographic factors, such as age and gender, that are crucial for accurately modeling subjectivity in language. When evaluating prediction models using the few available datasets, the results are highly unstable across both datasets and models. Our findings indicate that the volume of data is insufficient to significantly improve a language's prospects in NLP.

Figures

Figures reproduced from arXiv: 2509.05719 by the authors.

Figure 1
Figure 1. Distribution of papers considered in our survey by year and tasks (EA: Emotion Analysis, SA: Sentiment Analysis, and TD: Toxicity Detection). TD11 [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗

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Reference graph

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Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.