Leveraging Large Language Models for Accurate Sign Language Translation in Low-Resource Scenarios

Aldo Gangemi; Gabriele Tuccio; Luana Bulla; Misael Mongiov\`i

REVIEW 4 major objections 2 minor 43 references

Leveraging Large Language Models for Accurate Sign Language Translation in Low-Resource Scenarios

T0 review · 4 major / 2 minor · reviewed 2026-08-05 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read AulSign, by pairing signs with compact natural-language descriptions and dynamically selecting in-context examples, outperforms trained baselines for sign-language translation in low-data settings.

desk verdict A plausible low-resource SLT method, but as submitted the manuscript is unreadable mojibake, so the SOTA claim is unverifiable; worth a real look only after a clean resubmission. read the letter →

arxiv 2508.18183 v2 pith:ZKCUHZNO submitted 2025-08-25 cs.CL cs.AIcs.CY

Luana Bulla , Gabriele Tuccio , Misael Mongiovì , Aldo Gangemi This is my paper

classification cs.CLcs.AIcs.CY

keywords signlanguagetranslationlargemodelsin-contextlearningdynamicpromptinglow-resourceNLPassociationBank+LaCAM

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

The paper sets out to establish that a purely prompting-based method, AulSign, outperforms trained state-of-the-art baselines for translating natural language into sign language when parallel training data is scarce. AulSign works around the fact that LLMs have no intrinsic sign-language knowledge by pairing each sign with a compact natural-language description and instructing the model to emit those descriptions, while dynamically selecting instructive in-context examples for each input. The method is evaluated on English (SignBank+) and Italian (LaCAM), with reported results in the low-data regime. If correct, sign-language translation can be bootstrapped from a sign-description lexicon and a handful of parallel examples, without training a dedicated translation model.

What carries the argument

The sign-association mechanism is the load-bearing object. AulSign maintains a bank in which each sign is paired with a compact natural-language description, and at inference time the LLM is prompted with the bank plus a few dynamically selected exemplar translations. This is what lets a text-only LLM, which has no native sign-language knowledge, emit sign-language output: the target vocabulary is replaced by the description vocabulary. The second mechanism is dynamic in-context selection, which chooses examples whose source text resembles the current input; this matters when the bank is large and the prompt can hold only a subset of examples.

What would settle it

Have native deaf signers rate AulSign's outputs on a held-out set of sentences whose meaning depends on facial expression, spatial location, or simultaneous non-manual marking, and compare those ratings with a trained gloss-based baseline; if the prompt-based outputs are rated no more comprehensible than arbitrary sign-order shuffles, the description-bank representation does not preserve the information needed for translation.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that a large language model, given a bank that maps signs to short natural-language descriptions, can translate natural-language sentences into sign-language sequences through dynamic prompting: the prompt includes the description bank and a small set of in-context examples selected for closeness to the current input. This turns translation into a text-generation task the LLM can perform with no task-specific training. The reported experiments show superior performance relative to state-of-the-art trained models on SignBank+ (English) and LaCAM (Italian) under low-data conditions. The contribution is therefore the combination of sign associa

Load-bearing premise

The claim rests on the premise that a sign language can be adequately represented by a bank of compact natural-language descriptions of individual signs, which leaves out non-manual markers, spatial grammar, and simultaneity that carry grammatical meaning.

Editorial extensions

If this is right

Sign-language translation can be prototyped for a new language from a sign-description lexicon and a handful of parallel examples, with no model training required.
The approach transfers across at least two very different source languages (English and Italian), suggesting it is not tied to one linguistic family.
In data-scarce settings, LLM prompting may be the practical route until larger parallel sign-language corpora exist.
The quality ceiling shifts from model capacity to the completeness and precision of the sign-description bank.

Reading between the lines

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

If the benchmark evaluation scores predicted description sequences against closed-vocabulary glosses, the reported edge may partly reflect lexical selection; a comprehension study with deaf signers would test whether the sequences are usable sign language.
The same pipeline could be applied to other under-resourced sign languages by replacing only the description bank, which would separate the sign-association contribution from the prompting contribution.
Ablating dynamic sample selection against random or fixed in-context examples would reveal whether per-input selection is doing the work or whether a static prompt suffices.

Share X Bluesky LinkedIn Reddit HN

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

Here's the short version: the abstract describes a sensible idea—AulSign, an LLM-based prompt-and-select method for translating text to sign language, using dynamic prompting, in-context example selection, and a bank of compact sign descriptions. If it works as claimed, beating trained baselines on SignBank+ and LaCAM with little data would be a real accessibility win for under-resourced sign languages. The problem is that I can't check any of it. The full text is garbled—mojibake throughout—and the one legible line is a different arXiv ID from cond-mat.soft. So no methods, no tables, no baselines, no metrics exist in front of me. The 'superior performance' claim in the abstract is just an assertion.

What's genuinely new: the assembly. In-context learning with sample selection and dynamic prompts are established NLP techniques, but combining them with a sign-description bank to turn sign language translation into a closed-vocabulary selection task is a specific twist I haven't seen in the SLT literature. The abstract's admission that LLMs have no intrinsic sign-language knowledge and need the bank is honest framing.

The soft spots, in proportion. First, the unreadable text is the load-bearing flaw: without it, there's nothing to referee. Second, the reader's circularity concern is legitimate. If the model can only emit signs from a bank the authors built, and the evaluation rewards gloss-sequence match against that bank, then the score partly measures bank coverage and retrieval, not usable sign language. Non-manual markers, spatial grammar, and simultaneity are lost, which is a known problem for gloss-based sign processing. That said, it's not automatically fatal: if the evaluation also includes signer ratings or the task is deliberately defined as gloss selection for low-resource settings, it's a defensible modeling trade-off. I'd want the referee to check the bank construction, the sample-selection mechanism, and the evaluation metric closely. Third, the abstract's lack of numbers is annoying but common.

The paper is for NLP researchers working on sign language accessibility and low-resource translation. I'd take the idea seriously if the authors sent a readable manuscript. As submitted, the editor should return it for a clean text; no referee can review mojibake. After a clean resubmission, yes, send it to reviewers who know SLT metrics and can judge the closed-vocabulary formulation. I wouldn't cite it in its current form.

Referee Report

4 major / 2 minor

Summary. The manuscript proposes AulSign, a method that uses large language models with dynamic prompting, in-context learning, and a sign-description association bank to translate natural language into sign language in low-resource settings. The authors evaluate on English (SignBank+) and Italian (LaCAM CNR-ISTC) and claim superior performance compared to state-of-the-art models in a low-data scenario. However, the submitted full text is unreadable mojibake, with an embedded line referencing a different arXiv identifier (arXiv:2508.18184v1 [cond-mat.soft]). No method details, equations, tables, experimental results, baselines, metrics, or ablations can be inspected. The only substantive evidence for the central claim is the abstract's unsupported assertion of superiority.

Significance. If the claim were substantiated, it would be noteworthy: a purely prompting-based LLM method with an author-built sign-description bank might outperform trained baselines for spoken-to-sign translation on two low-resource benchmarks. The sign-association idea is practically appealing and the choice of two languages and datasets is a reasonable first test. However, as submitted, the paper provides no checkable evidence whatsoever. There is no readable methods section, no results table, no baseline comparison, no code, and no data release that could partially offset the missing text. The significance of the contribution is therefore entirely conditional on a complete resubmission.

major comments (4)

[Full Text (entire body)] The submitted full text is unreadable mojibake. It is impossible to inspect any equation, table, algorithm, experimental setup, or quantitative result. The only legible line, 'arXiv:2508.18184v1 [cond-mat.soft] 25 Aug 2025', identifies a different paper and subject area. This blocks all verification of the central claim of superior performance. A resubmission must contain a readable, complete manuscript with methods, evaluation, baselines, and results.
[Abstract] The abstract states that AulSign achieves 'superior performance compared to state-of-the-art models in low-data scenario' without reporting any scores, baselines, effect sizes, or evaluation metrics. This is an unsupported empirical claim. Even if the full text were legible, the abstract alone provides no way to assess the magnitude or statistical reliability of the claimed improvement.
[Sign-description association bank / evaluation] The method defines the output space through a sign-description association bank: the LLM can only emit signs from descriptions the authors supply. If the evaluation metric measures matching against reference gloss sequences drawn from the same bank, high scores may reflect closed-vocabulary lexical selection and bank coverage rather than usable sign-language translation. Non-manual markers, spatial grammar, and simultaneity are not represented in this output space. The manuscript must specify the exact evaluation metric and should include human evaluation or an assessment of whether the outputs constitute grammatical, usable sign language.
[Low-resource experimental setting] The phrase 'low-data scenario' is undefined. The manuscript should state the number of training examples (or in-context examples) used for each language and dataset, the data split, how the in-context examples are selected, and the exact baselines and their training conditions. Without this information, the claimed advantage over state-of-the-art models cannot be contextualized or reproduced.

minor comments (2)

[Full Text] The embedded line 'arXiv:2508.18184v1 [cond-mat.soft] 25 Aug 2025' appears to be a document-assembly artifact; it must be removed and the correct arXiv identifier used.
[References/Datasets] Because the full text is garbled, dataset citations and version information for SignBank+ and LaCAM cannot be verified. A revised submission should include precise references and dataset versions.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity can be established: the abstract describes a closed-vocabulary prompting method, but the full text is garbled and no derivation chain, evaluation metric, or self-citation chain is inspectable.

full rationale

The only legible content is the abstract. Its method—'we associate the signs with compact descriptions in natural language and instruct the model to use them'—defines the output vocabulary as a description bank, but it does not by itself show that the reported 'superior performance' is forced by construction. There is no visible equation, table, baseline setup, or metric definition in the provided full text, which is mojibake and even contains the inserted line 'arXiv:2508.18184v1 [cond-mat.soft] 25 Aug 2025', an arXiv identifier for a different paper. This document-assembly anomaly prevents verification, but it is not evidence of circularity. No self-citation is visible, no fitted parameter is renamed as a prediction, and no uniqueness theorem is invoked. Under the rule that circularity must be exhibited with a specific reduction (Eq. X = Eq. Y by construction, or a fitted parameter called a prediction), I find no circular step that can be quoted and demonstrated. The reader's concern that success may reflect description-bank coverage rather than translation skill is a validity and evaluation risk, not a demonstrated circularity, and the abstract's use of external benchmarks (SignBank+, LaCAM) suggests the evaluation was at least intended to be independent. Therefore the honest non-finding is score 0.

Assumptions & free parameters 5 free parameters · 4 assumptions · 1 invented entities

The method rests on an engineering stack whose components are not specified at abstract level: the size and construction of the sign-description bank, the sample-selection scheme, the number of in-context examples, the prompt template, and the base LLM. Each is a genuine free choice that can change results. The axioms are the substantive assumptions: that gloss-plus-description is a faithful enough encoding of sign language, that the benchmarks are valid ground truth, and that LLM in-context learning can do the task. The paper introduces no theoretical entities; its invented artifact is the description bank, treated here as a free parameter because coverage and provenance are unstated.

free parameters (5)

number of in-context examples (k)
Sample-selection budget in the dynamic prompting step; abstract does not report its value or sensitivity.
sample selection scoring metric
How the method picks which few-shot examples to include; choice affects performance and is not described in the abstract.
sign description bank (gloss-to-text mapping)
Hand-built or derived resource associating each sign with a compact description; its coverage and construction are load-bearing and unstated at abstract level.
base LLM choice
The specific LLM used for prompting influences all results; not stated in the abstract.
prompt template / dynamic prompt strategy
The dynamic prompting scheme has internal design choices not detailed in the abstract.

assumptions (4)

ad hoc to paper Sign languages can be adequately represented as ordered sequences of compact natural-language sign descriptions.
The sign-association step assumes gloss-plus-description captures enough linguistic content for usable translation; non-manual and spatial grammar are not represented.
domain assumption Benchmark annotations (SignBank+, LaCAM) are a valid ground truth for sign language translation quality.
The evaluation treats these corpora as reference translations; annotation conventions and metric choice are not visible in the abstract.
domain assumption LLMs can perform sign-language translation via in-context learning when given descriptions and examples.
The whole method rests on few-shot capability of LLMs for this task, which is exactly what is being tested.
domain assumption The evaluation metric measures translation quality comparable to human judgment.
Abstract reports 'superior performance' without stating the metric (e.g., BLEU, ROUGE, gloss accuracy) or human evaluation.

invented entities (1)

Sign-description association bank
purpose: Maps each sign to a compact natural-language description so an LLM, which 'lack[s] intrinsic knowledge of sign languages', can emit signs as text.
The abstract gives no details on construction, size, coverage, or how it avoids leaking test-set signs into the bank; therefore it is a postulated resource with no external falsifiable handle in this submission.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Leveraging Large Language Models for Accurate Sign Language Translation in Low-Resource Scenarios." pith.science (2026). https://pith.science/paper/ZKCUHZNO

@misc{pith2026250818183,
  author       = {Pith},
  title        = {Pith review of: Leveraging Large Language Models for Accurate Sign Language Translation in Low-Resource Scenarios},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZKCUHZNO}},
  note         = {Machine review of arXiv:2508.18183}
}

read the original abstract

Translating natural languages into sign languages is a highly complex and underexplored task. Despite growing interest in accessibility and inclusivity, the development of robust translation systems remains hindered by the limited availability of parallel corpora which align natural language with sign language data. Existing methods often struggle to generalize in these data-scarce environments, as the few datasets available are typically domain-specific, lack standardization, or fail to capture the full linguistic richness of sign languages. To address this limitation, we propose Advanced Use of LLMs for Sign Language Translation (AulSign), a novel method that leverages Large Language Models via dynamic prompting and in-context learning with sample selection and subsequent sign association. Despite their impressive abilities in processing text, LLMs lack intrinsic knowledge of sign languages; therefore, they are unable to natively perform this kind of translation. To overcome this limitation, we associate the signs with compact descriptions in natural language and instruct the model to use them. We evaluate our method on both English and Italian languages using SignBank+, a recognized benchmark in the field, as well as the Italian LaCAM CNR-ISTC dataset. We demonstrate superior performance compared to state-of-the-art models in low-data scenario. Our findings demonstrate the effectiveness of AulSign, with the potential to enhance accessibility and inclusivity in communication technologies for underrepresented linguistic communities.

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

43 extracted references · 33 canonical work pages

[1]

A. M. Almasoud and H. S. Al-Khalifa. A proposed semantic machine translation system for translating arabic text to arabic sign language. In Proceedings of the Second Kuwait Conference on e-Services and e-Systems, pages 1--6, 2011

work page 2011
[2]

A. M. Almasoud and H. S. Al-Khalifa. Semsignwriting: A proposed semantic system for arabic text-to-signwriting translation. 2012

work page 2012
[3]

Angelova, E

G. Angelova, E. Avramidis, and S. M \"o ller. Using neural machine translation methods for sign language translation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 273--284, 2022

work page 2022
[4]

Aziz and A

M. Aziz and A. Othman. Evolution and trends in sign language avatar systems: Unveiling a 40-year journey via systematic review. Multimodal Technologies and Interaction, 7 0 (10): 0 97, 2023

work page 2023
[5]

Bonsignori, A

C. Bonsignori, A. Di Renzo, G. Gianfreda, T. Lucioli, B. Pennacchi, L. Lamano, and O. Capirci. A visually annotated multimodal corpus of lis, international workshop multimodal language: theoretical perspectives and research methods. In Proceedings of the 2019 international symposium on signal processing systems, 2025

work page 2019
[6]

T. B. Brown. Language models are few-shot learners. arXiv preprint arXiv:2005.14165, 2020

arXiv 2005
[7]

Bulla, G

L. Bulla, G. Tuccio, M. Mongiovì, and A. Gangemi. Leveraging large language models for accurate sign language translation in low-resource scenarios. In ECAI 2025. IOS Press, 2025

work page 2025
[8]

J. S. Daniel and A. Pal. Impact of non-standard unicode characters on security and comprehension in large language models. arXiv preprint arXiv:2405.14490, 2024

arXiv 2024

Show all 43 references

[9]

de Almeida Freitas, S

F. de Almeida Freitas, S. M. Peres, O. de Paula Albuquerque, and M. Fantinato. Leveraging sign language processing with formal signwriting and deep learning architectures. In Brazilian Conference on Intelligent Systems, pages 299--314. Springer, 2023

2023
[10]

De Coster, D

M. De Coster, D. Shterionov, M. Van Herreweghe, and J. Dambre. Machine translation from signed to spoken languages: State of the art and challenges. Universal Access in the Information Society, 23 0 (3): 0 1305--1331, 2024

2024
[11]

Farooq, M

U. Farooq, M. S. M. Rahim, N. Sabir, A. Hussain, and A. Abid. Advances in machine translation for sign language: approaches, limitations, and challenges. Neural Computing and Applications, 33 0 (21): 0 14357--14399, 2021

2021
[12]

Fayyazsanavi, A

P. Fayyazsanavi, A. Anastasopoulos, and J. Ko s eck \'a . Gloss2text: Sign language gloss translation using llms and semantically aware label smoothing. arXiv preprint arXiv:2407.01394, 2024

2024 arXiv
[13]

Garc \' a-Mart \' nez, L

M. Garc \' a-Mart \' nez, L. Barrault, and F. Bougares. Factored neural machine translation architectures. In Proceedings of the 13th International Conference on Spoken Language Translation, 2016

2016
[14]

J. Gong, L. G. Foo, Y. He, H. Rahmani, and J. Liu. Llms are good sign language translators. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18362--18372, 2024

2024
[15]

M. U. Hadi, Q. Al Tashi, A. Shah, R. Qureshi, A. Muneer, M. Irfan, A. Zafar, M. B. Shaikh, N. Akhtar, J. Wu, et al. Large language models: a comprehensive survey of its applications, challenges, limitations, and future prospects. Authorea Preprints, 2024

2024
[16]

E. J. Hwang, S. Cho, J. Lee, and J. C. Park. An efficient sign language translation using spatial configuration and motion dynamics with llms. arXiv preprint arXiv:2408.10593, 2024

2024 arXiv
[17]

Jiang, A

Z. Jiang, A. Moryossef, M. M \"u ller, and S. Ebling. Machine translation between spoken languages and signed languages represented in signwriting. arXiv preprint arXiv:2210.05404, 2022

2022 arXiv
[18]

Jiang, G

Z. Jiang, G. Sant, A. Moryossef, M. M \"u ller, R. Sennrich, and S. Ebling. Signclip: Connecting text and sign language by contrastive learning. arXiv preprint arXiv:2407.01264, 2024

2024 arXiv
[19]

L. E. Johnson and S. Rashad. An innovative system for real-time translation from american sign language (asl) to spoken english using a large language model (llm). In 2024 IEEE 15th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), pages 605-...

2024
[20]

Z. Kang. Spoken language to sign language translation system based on hamnosys. In Proceedings of the 2019 international symposium on signal processing systems, pages 159--164, 2019

2019
[21]

Koehn and H

P. Koehn and H. Hoang. Factored translation models. In Proceedings of the 2007 joint conference on empirical methods in natural language processing and computational natural language learning (EMNLP-CoNLL), pages 868--876. Association for Computational Linguistics, 2007

2007
[22]

Lee, J.-H

H. Lee, J.-H. Kim, E. J. Hwang, J. Kim, and J. C. Park. Leveraging large language models with vocabulary sharing for sign language translation. In 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW), pages 1--5. IEEE, 2023

2023
[23]

S. Lee, A. Shakir, D. Koenig, and J. Lipp. Open source strikes bread - new fluffy embeddings model, 2024. URL https://www.mixedbread.ai/blog/mxbai-embed-large-v1

2024
[24]

J. Lim, I. Sa, B. MacDonald, and H. S. Ahn. A sign language recognition system with pepper, lightweight-transformer, and llm. arXiv preprint arXiv:2309.16898, 2023

2023 arXiv
[25]

Y. Liu, W. Zhang, S. Ren, C. Huang, J. Yu, and L. Xu. Scope: Sign language contextual processing with embedding from llms. arXiv preprint arXiv:2409.01073, 2024

2024 arXiv
[26]

Matsumoto, M

T. Matsumoto, M. Kato, and T. Ikeda. Jspad: A sign language writing tool using signwriting. In proceedings of the 3rd international universal communication symposium, pages 363--367, 2009

2009
[27]

Moryossef and Z

A. Moryossef and Z. Jiang. Signbank+: Multilingual sign language translation dataset. arXiv preprint arXiv:2309.11566, 2023

2023 arXiv
[28]

Moryossef, Z

A. Moryossef, Z. Jiang, M. M \"u ller, S. Ebling, and Y. Goldberg. Linguistically motivated sign language segmentation. arXiv preprint arXiv:2310.13960, 2023

2023 arXiv
[29]

Papineni, S

K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pages 311--318, 2002

2002
[30]

Pavlakos, V

G. Pavlakos, V. Choutas, N. Ghorbani, T. Bolkart, A. A. Osman, D. Tzionas, and M. J. Black. Expressive body capture: 3d hands, face, and body from a single image. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10975--10985, 2019

2019
[31]

Popovi \'c

M. Popovi \'c . chrf: character n-gram f-score for automatic mt evaluation. In Proceedings of the tenth workshop on statistical machine translation, pages 392--395, 2015

2015
[32]

Prillwitz and H

S. Prillwitz and H. Zienert. Hamburg notation system for sign language: Development of a sign writing with computer application. In Current trends in European Sign Language Research. Proceedings of the 3rd European Congress on Sign Language Research, pages 355--379, 1990

1990
[33]

Reimers and I

N. Reimers and I. Gurevych. Sentence-bert: Sentence embeddings using siamese bert-networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 11 2019. URL http://arxiv.org/abs/1908.10084

2019 arXiv
[34]

Reimers and I

N. Reimers and I. Gurevych. Making monolingual sentence embeddings multilingual using knowledge distillation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 11 2020. URL https://arxiv.org/ab...

2020 arXiv
[35]

A. F. Sevilla, A. D. Esteban, and J. M. Lahoz-Bengoechea. Automatic signwriting recognition: Combining machine learning and expert knowledge to solve a novel problem. IEEE Access, 11: 0 13211--13222, 2023

2023
[36]

V. Sutton. Lessons in SignWriting. SignWriting Press, 2022

2022
[37]

Tuccio, L

G. Tuccio, L. Bulla, M. Madonia, A. Gangemi, and M. Mongiovì. GRAMMAR-LLM : Grammar-constrained natural language generation. In Findings of the Association for Computational Linguistics: ACL 2025, pages 3412--3422, 2025

2025
[38]

L. Wang, C. Lyu, T. Ji, Z. Zhang, D. Yu, S. Shi, and Z. Tu. Document-level machine translation with large language models. arXiv preprint arXiv:2304.02210, 2023

2023 arXiv
[39]

R. Wong, N. C. Camgoz, and R. Bowden. Sign2gpt: Leveraging large language models for gloss-free sign language translation. arXiv preprint arXiv:2405.04164, 2024

2024 arXiv
[40]

Yin and J

K. Yin and J. Read. Better sign language translation with stmc-transformer. arXiv preprint arXiv:2004.00588, 2020

2004 arXiv
[41]

Z. Yu, S. Huang, Y. Cheng, and T. Birdal. Signavatars: A large-scale 3d sign language holistic motion dataset and benchmark. In European Conference on Computer Vision, pages 1--19. Springer, 2025

2025
[42]

Zhang, B

B. Zhang, B. Haddow, and A. Birch. Prompting large language model for machine translation: A case study. In International Conference on Machine Learning, pages 41092--41110. PMLR, 2023

2023
[43]

B. Zhou, Z. Chen, A. Clap \'e s, J. Wan, Y. Liang, S. Escalera, Z. Lei, and D. Zhang. Gloss-free sign language translation: Improving from visual-language pretraining. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 20871--20881, 2023

2023

Pith tools

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

[1] [1]

A. M. Almasoud and H. S. Al-Khalifa. A proposed semantic machine translation system for translating arabic text to arabic sign language. In Proceedings of the Second Kuwait Conference on e-Services and e-Systems, pages 1--6, 2011

work page 2011

[2] [2]

A. M. Almasoud and H. S. Al-Khalifa. Semsignwriting: A proposed semantic system for arabic text-to-signwriting translation. 2012

work page 2012

[3] [3]

Angelova, E

G. Angelova, E. Avramidis, and S. M \"o ller. Using neural machine translation methods for sign language translation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 273--284, 2022

work page 2022

[4] [4]

Aziz and A

M. Aziz and A. Othman. Evolution and trends in sign language avatar systems: Unveiling a 40-year journey via systematic review. Multimodal Technologies and Interaction, 7 0 (10): 0 97, 2023

work page 2023

[5] [5]

Bonsignori, A

C. Bonsignori, A. Di Renzo, G. Gianfreda, T. Lucioli, B. Pennacchi, L. Lamano, and O. Capirci. A visually annotated multimodal corpus of lis, international workshop multimodal language: theoretical perspectives and research methods. In Proceedings of the 2019 international symposium on signal processing systems, 2025

work page 2019

[6] [6]

T. B. Brown. Language models are few-shot learners. arXiv preprint arXiv:2005.14165, 2020

arXiv 2005

[7] [7]

Bulla, G

L. Bulla, G. Tuccio, M. Mongiovì, and A. Gangemi. Leveraging large language models for accurate sign language translation in low-resource scenarios. In ECAI 2025. IOS Press, 2025

work page 2025

[8] [8]

J. S. Daniel and A. Pal. Impact of non-standard unicode characters on security and comprehension in large language models. arXiv preprint arXiv:2405.14490, 2024

arXiv 2024