Comparative Study of Pre-Trained BERT and Large Language Models for Code-Mixed Named Entity Recognition

Amey Shembade; Madhushri Wagh; Mayur Shirke; Pavan Thorat; Raviraj Joshi

REVIEW 3 major objections 5 minor 24 references

Comparative Study of Pre-Trained BERT and Large Language Models for Code-Mixed Named Entity Recognition

T0 review · 3 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read Fine-tuned code-mixed BERT models beat zero-shot LLMs on Hinglish NER.

desk verdict Useful benchmark snapshot with a new Gemini zero-shot datapoint, but the headline ranking lacks error bars and is likely within noise; worth reviewing with required revisions. read the letter →

arxiv 2509.02514 v1 pith:MXL5MDQV submitted 2025-09-02 cs.CL cs.LG

Mayur Shirke , Amey Shembade , Pavan Thorat , Madhushri Wagh , Raviraj Joshi This is my paper · ORCID

classification cs.CLcs.LG

keywords code-mixedNERHinglishBERTnamedentityrecognitionzero-shotLLMdomain-specificpretrainingmultilingualmodelsHindi-English

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

This paper sets out to show that for named entity recognition (NER) in Hindi-English code-mixed text, models fine-tuned on code-mixed data beat both general multilingual transformers and zero-shot large language models. It compares three Hinglish-tuned BERT variants (HingBERT, HingMBERT, HingRoBERTa) against four non-code-mixed baselines (BERT, RoBERTa, IndicBERT, MuRIL) and Gemini on a 3,637-sentence benchmark. The best model, HingBERT, reaches an entity-level F1 of 79.74, while the strongest non-code-mixed baseline MuRIL reaches 77.50 and zero-shot Gemini reaches 62.24. The authors attribute the gap to domain-specific pretraining on real code-mixed corpora, arguing that general multilingual pretraining transfers only partially to informal, transliterated, code-switched text. A reader should care because code-mixed content is pervasive in multilingual societies, and the result clarifies when specialized models are still worth training rather than relying on large general-purpose LLMs.

What carries the argument

The argument is carried by the comparison of three families of models on one benchmark: code-mixed fine-tuned transformers (HingBERT, HingMBERT, HingRoBERTa), non-code-mixed multilingual transformers (BERT Base Cased, RoBERTa, IndicBERT, MuRIL), and a zero-shot LLM (Gemini). The load-bearing mechanism is domain-specific pretraining and fine-tuning on Hindi-English code-mixed corpora, which exposes the model to transliteration, informal grammar, and language switching that standard multilingual corpora lack. All models are fine-tuned using a hyperparameter search and evaluated with entity-level F1 rather than token-level accuracy, so the reported gap is specifically about the ability to recognize complete named entities rather than individual tokens.

What would settle it

Run a bootstrap test over the 3,637 sentences with repeated 80/20 splits: if the 95% confidence intervals for entity-level F1 of HingBERT (79.74) and MuRIL (77.50) overlap, the claimed advantage of code-mixed pretraining over MuRIL is not significant at this dataset size.

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Extended reading notes

Core claim

The central discovery is that code-mixed fine-tuned transformer models consistently outperform both non-code-mixed multilingual models and a zero-shot generative LLM on Hinglish NER. On the held-out 20% test set, HingBERT achieves entity-level F1 79.74, HingMBERT 78.87, and HingRoBERTa 78.39, compared with 77.50 for MuRIL, 76.47 for IndicBERT, 76.24 for RoBERTa, and 72.12 for BERT Base Cased. Gemini, evaluated zero-shot with entity labels removed, reaches 62.24 F1. The authors interpret this as evidence that domain-specific pretraining on code-mixed data, rather than model scale or general multilingual coverage, is the decisive factor for structured prediction in code-mixed text. They also note that Gemini's competitive accuracy but much lower precision and recall indicates LLMs still struggle with fine-grained entity classification without supervision.

Load-bearing premise

The central ranking assumes that F1 differences of around two points on a single 80/20 split of 3,637 sentences are meaningful, even though the test set has very few examples of rare entity types and no confidence intervals are reported.

Editorial extensions

If this is right

Teams building NER for Hindi-English social media text should fine-tune code-mixed BERT variants rather than rely on generic multilingual transformers.
Zero-shot LLMs are not yet a drop-in replacement for task-specific models on structured prediction in code-mixed text, at least without additional prompting or adaptation.
Domain-specific pretraining on real code-mixed data yields a larger benefit than switching to a more powerful general model for this task.
The two-point edge of HingBERT over MuRIL suggests the advantage of code-mixed pretraining is real but modest, and larger benchmarks are needed to confirm its size.

Reading between the lines

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

Inference: Because the dataset has only 90 I-Org and 31 I-Loc labels in total, the reported entity-level F1 is driven almost entirely by person entities and beginning tags; a per-tag breakdown could reveal that code-mixed pretraining's advantage does not extend to rare inside-organization or inside-location spans.
Inference: The paper reports no variance estimates, so the two-point gap between HingBERT and MuRIL could be within run-to-run noise; a bootstrap resampling of the 3,637 sentences with repeated splits would test whether the ranking is stable.
Inference: The zero-shot setup uses a single prompt style; testing Gemini with few-shot exemplars or constrained output decoding might close much of the 17-point gap and would clarify whether the deficit is inherent to LLMs or an artifact of the prompting strategy.
Inference: If the pattern transfers, other code-mixed pairs such as Marathi-English will likely see larger NER gains from building a small code-mixed pretraining corpus than from adopting a larger general-purpose multilingual model.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Bottom line: this is a serviceable benchmark comparison, not a new mechanism. It confirms what the same group already reported in Patil et al. (2023) and Nayak and Joshi (2022): HingBERT-family models beat generic multilingual BERTs on Hinglish NER. The genuinely new pieces are the zero-shot Gemini evaluation (62.2 F1, far below the fine-tuned models) and the inclusion of IndicBERT, MuRIL, HingMBERT, and HingRoBERTa on this dataset. The paper is clearly written, uses entity-level seqeval metrics, and does a reasonable Optuna hyperparameter search. The authors also state their limitations explicitly, including the small dataset.

The soft spot is the missing uncertainty analysis. Section 4.2 says each run used three seeds and the average was reported, but no standard deviations or confidence intervals appear anywhere. The test F1 gap between HingBERT (79.74) and MuRIL (77.50) is 2.24 points; with roughly 866 entity spans in the test set, that is around 19 spans. Without variance, we cannot tell whether that gap is signal or noise. The validation table makes this worse: among the code-mixed models, HingMBERT is first on validation and third on test, while HingBERT moves from third to first. The spread among these models is about 1.4 points, similar to the gap to MuRIL, so the exact ordering is unstable. The dataset also has very few rare tags (I-Org: 90, I-Loc: 31 in the whole corpus), so the test set probably contains only a handful of those, making per-type conclusions shaky. No code, no data split, no Gemini prompt or post-processing details, and the dataset is not named; reproducibility is limited.

The broad conclusion that code-mixed models as a group beat generic multilingual models is probably right—it is consistent with prior work and the direction is stable. But attributing the gap to domain-specific pretraining is an interpretation, not a demonstrated effect. The comparison is fine-tuning on the same training set from different starting points; the authors need to show the gap is robust.

Who this is for: practitioners working on Hinglish NER who want a quick model comparison and a Gemini zero-shot datapoint. It does not change methodology. If I were an editor, I would send it to review—the work is honest and the Gemini result is worth putting on record—but I would require revisions that add error bars or significance testing, release code and the exact split, and soften the causal claim. A referee should not desk-reject it, but should not accept it as is.

Referee Report

3 major / 5 minor

Summary. The paper presents a comparative evaluation of three families of models for Hindi-English code-mixed named entity recognition: code-mixed fine-tuned BERT variants (HingBERT, HingMBERT, HingRoBERTa), non-code-mixed multilingual models (BERT Base Cased, IndicBERT, RoBERTa, MuRIL), and a zero-shot generative LLM (Google Gemini). All models are evaluated on a 3,637-sentence Hinglish NER dataset using an 80/20 train/test split, with entity-level precision, recall, and F1 computed via seqeval. The reported test F1 scores place HingBERT first at 79.74, followed by HingMBERT (78.87), HingRoBERTa (78.39), and the best non-code-mixed model MuRIL (77.50), with Gemini at 62.24. The paper attributes this gap to domain-specific code-mixed pretraining and argues that specialized models still outperform zero-shot LLMs.

Significance. If the reported ranking is statistically robust, the paper provides a useful empirical reference for code-mixed NER, confirming the practical value of domain-specific pretraining over generic multilingual models and quantifying the current zero-shot performance ceiling of a commercial LLM. The use of Optuna-tuned hyperparameters for each model and the entity-level evaluation protocol are commendable. However, the significance is tempered by several issues: there is no uncertainty quantification despite the claim of three-seed averaging, the test-set differences are small in absolute terms, the dataset provenance is not clearly identified, and the paper itself concedes the dataset is relatively small. These limitations directly affect the strength of the central attribution claim.

major comments (3)

[§4.2, Tables 2–3, Limitations] The central comparative claim rests on gaps measured in entity-level F1 points (HingBERT 79.74 vs. MuRIL 77.50 on the test set, Table 3), but the paper reports only averages from three seeds with no standard deviations, confidence intervals, or significance tests anywhere. Based on the label counts in Table 1 and the 80/20 split, the test set contains on the order of 866 entity spans; a 2.24-point gap corresponds to roughly 19 spans, which is not clearly beyond seed-level or data-sampling noise. This concern is reinforced by the validation/test ranking reversal: HingMBERT is first on validation (77.14, Table 2) but third among code-mixed models on test (78.87, Table 3), while HingBERT moves from third on validation (76.51) to first on test (79.74). The paper's Limitations section itself acknowledges the 'relatively small dataset size.' I request that the authors report per-seed results, standard deviations, and a statistical significance test (e.g., a bootstrap over test sentences or a paired test) before the claim that code-mixed pretraining explains the gap can be accepted.
[§3.1, Table 1] The dataset is described only as 'a benchmark Hinglish NER dataset,' but its source is never explicitly identified or cited. This is a reproducibility issue: the reader cannot verify the split, compare with prior results on the same benchmark, or assess the claim that this is a standard evaluation setting. The authors should state whether this is the Singh et al. (2018) corpus or another dataset, describe how the 80/20 split was generated (random seed, stratification), and document any preprocessing applied to the original corpus. Additionally, Table 1 shows that the full corpus contains only 90 I-Org and 31 I-Loc instances; the test set therefore contains very few examples of these rare tags. The paper should report per-entity F1 with confidence intervals for these categories, as the current single macro/entity-level F1 may be dominated by frequent types.
[§3.6, §4.3] The zero-shot Gemini evaluation is not reproducible as described. The paper does not provide the prompt template, the exact model version or access date, decoding parameters (temperature, max tokens), or the post-processing and alignment procedure used to convert Gemini's free-form output into BIO tags aligned with the original token boundaries. Since the Gemini result (F1 62.24) is a reported contribution and is compared against fine-tuned models, these details are essential. I request the authors include the full prompt, the parsing pipeline, and any failure cases where the model output could not be aligned.

minor comments (5)

[§5, Tables 2–3] The text says 'Among them, HingMBERT achieves the highest F1-score' without specifying the evaluation set; this is true only on the validation set, while on the test set HingBERT is highest. Please clarify.
[§3.3, Tables 2–3] The model name is spelled inconsistently as 'Hing-mBERT' in Section 3.3 and 'HingMBERT' in the tables; please unify the nomenclature.
[§3.5, §5] The paper mentions 'macro-averaged precision and F1-score' in the results discussion, but the tables do not state the averaging scheme used by seqeval; please specify whether the reported F1 is a macro-average over entity types and whether 'Accuracy' is token-level accuracy with masked subwords.
[Tables 2 and 3] The numerical formatting is inconsistent (e.g., 96.88 vs. 97.38 with different decimal precision); please use a consistent number of decimal places throughout.
[§3.4, §4.2] The relationship between the validation set used for Optuna optimization and early stopping and the 80/20 split is not fully specified; please clarify whether the validation set is a subset of the training portion or a separate split, and report the validation-set construction method.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation; the comparison is an empirical evaluation on an external benchmark and does not reduce to its inputs.

full rationale

This paper is an empirical benchmark comparison, not a derivation with equations, so the characteristic circularity patterns (self-definitional equations, fitted inputs renamed as predictions, uniqueness theorems imported via self-citation, ansatz smuggling) do not apply. The central claim that code-mixed fine-tuned models such as HingBERT outperform non-code-mixed models and zero-shot Gemini is supported by test-set metrics computed in this paper against a fixed benchmark dataset, using publicly available model checkpoints. The cited prior work from the same group (e.g., Nayak and Joshi 2022, Patil et al. 2023) is used as background and to identify the models, but the ranking itself is generated here and can be checked externally, so the self-citations are not load-bearing. The acknowledged limitations—small dataset size, lack of significance tests or confidence intervals—bear on statistical robustness rather than on circularity, and a concern about error bars is a correctness-risk issue, not a circularity finding. No step in the paper reduces a claimed prediction to a fitted parameter or to a definitional identity.

Assumptions & free parameters 1 free parameters · 6 assumptions · 0 invented entities

The paper makes no mathematical derivation and introduces no new entities. Its central claim rests on empirical conventions: correct labels, a reliable split, fair tuning, and faithful alignment of LLM outputs. The only fitted values are per-model hyperparameters chosen by Optuna, and they are not reported.

free parameters (1)

Optuna-tuned hyperparameters (learning rate, batch size, weight decay, Adam epsilon, gradient accumulation steps… = not reported
Section 3.4 says 40 Optuna trials per model selected the configuration with highest validation F1, but the chosen values are never listed.

assumptions (6)

domain assumption The benchmark dataset's gold labels are correct and representative of Hinglish code-mixed social media text.
Section 3.1 treats the dataset as ground truth without naming or auditing it.
domain assumption The 80/20 random split yields a test set large enough for meaningful entity-level F1 comparison.
Section 3.1; no stratification or confidence intervals are given for rare tags.
domain assumption seqeval entity-level F1 is an appropriate and sufficient metric for this comparison.
Section 3.5; conclusions rest on F1 despite the highly imbalanced label distribution.
domain assumption Optuna hyperparameter search is equally effective for all models, so tuning differences do not explain the ranking.
Section 3.4; no per-model best configurations or learning curves are shown.
domain assumption Gemini's free-form outputs can be aligned to original BIO labels without systematic bias.
Sections 3.6 and 4.3; prompt, decoding settings, and alignment code are not provided.
domain assumption The pretrained model weights are the official public releases and fine-tuning follows standard Hugging Face practice.
Section 3.3; no model hashes or training logs are supplied.

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Cite this review

Pith. "Pith review of Comparative Study of Pre-Trained BERT and Large Language Models for Code-Mixed Named Entity Recognition." pith.science (2026). https://pith.science/paper/MXL5MDQV

@misc{pith2026250902514,
  author       = {Pith},
  title        = {Pith review of: Comparative Study of Pre-Trained BERT and Large Language Models for Code-Mixed Named Entity Recognition},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MXL5MDQV}},
  note         = {Machine review of arXiv:2509.02514}
}

read the original abstract

Named Entity Recognition (NER) in code-mixed text, particularly Hindi-English (Hinglish), presents unique challenges due to informal structure, transliteration, and frequent language switching. This study conducts a comparative evaluation of code-mixed fine-tuned models and non-code-mixed multilingual models, along with zero-shot generative large language models (LLMs). Specifically, we evaluate HingBERT, HingMBERT, and HingRoBERTa (trained on code-mixed data), and BERT Base Cased, IndicBERT, RoBERTa and MuRIL (trained on non-code-mixed multilingual data). We also assess the performance of Google Gemini in a zero-shot setting using a modified version of the dataset with NER tags removed. All models are tested on a benchmark Hinglish NER dataset using Precision, Recall, and F1-score. Results show that code-mixed models, particularly HingRoBERTa and HingBERT-based fine-tuned models, outperform others - including closed-source LLMs like Google Gemini - due to domain-specific pretraining. Non-code-mixed models perform reasonably but show limited adaptability. Notably, Google Gemini exhibits competitive zero-shot performance, underlining the generalization strength of modern LLMs. This study provides key insights into the effectiveness of specialized versus generalized models for code-mixed NER tasks.

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Reference graph

Works this paper leans on

24 extracted references · 19 canonical work pages

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online" 'onlinestring :=

ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRING...
[2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...
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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

[1] [1]

online" 'onlinestring :=

ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRING...

[2] [2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

[3] [3]

Gustavo Aguilar, Fahad AlGhamdi, Victor Soto, Mona Diab, Julia Hirschberg, and Thamar Solorio. 2018. Named entity recognition on code-switched data: Overview of the calcs 2018 shared task. In Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching, pages 138--147

work page 2018

[4] [4]

i am borrowing ya mixing?

Kalika Bali, Jatin Sharma, Monojit Choudhury, and Yogarshi Vyas. 2014. “i am borrowing ya mixing?" an analysis of english-hindi code mixing in facebook. In Proceedings of the first workshop on computational approaches to code switching, pages 116--126

work page 2014

[5] [5]

Somnath Banerjee, Sudip Kumar Naskar, Paolo Rosso, and Sivaji Bandyopadhyay. 2017. Named entity recognition on code-mixed cross-script social media content. Computaci \'o n y Sistemas , 21(4):681--692

work page 2017

[6] [6]

Bharathi Raja Chakravarthi, Navya Jose, Shardul Suryawanshi, Elizabeth Sherly, and John Philip McCrae. 2020 a . A sentiment analysis dataset for code-mixed malayalam-english. In Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL), p...

work page 2020

[7] [7]

Bharathi Raja Chakravarthi, Vigneshwaran Muralidaran, Ruba Priyadharshini, and John P McCrae. 2020 b . Corpus creation for sentiment analysis in code-mixed tamil-english text. arXiv preprint arXiv:2006.00206

work page Pith review arXiv 2020

[8] [8]

Tanmay Chavan, Omkar Gokhale, Aditya Kane, Shantanu Patankar, and Raviraj Joshi. 2023. My boli: Code-mixed marathi-english corpora, pretrained language models and evaluation benchmarks. In Findings of the Association for Computational Linguistics: IJCNLP-AACL 2023 (Findings), pages 242--249

work page 2023