Pith. sign in

REVIEW 4 major objections 4 minor 35 references

Teach Me Sign: Stepwise Prompting LLM for Sign Language Production

T0 review · 4 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Fine-tuning an off-the-shelf LLM with stepwise sign-language prompts produces pose sequences that beat Progressive Transformer by 18.7% on Phoenix14T and 20.9% on How2Sign.

desk verdict Plausible LLM-based sign language production, but the claimed contribution—GPT-4o-generated assistance—is never validated and the main baseline improvements exist even without it. read the letter →

arxiv 2507.10972 v1 pith:XOB7L3H5 submitted 2025-07-15 cs.CL cs.CVcs.MM

Zhaoyi An , Rei Kawakami This is my paper · ORCID
classification cs.CLcs.CVcs.MM
keywords signlanguageproductionlargemodelsstepwisepromptingVQ-VAEtokenizationLoRAfine-tuningtext-to-videogenerationPhoenix14THow2Sign
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 tries to establish that sign language production can be treated as another natural language task for an off-the-shelf large language model. Instead of relying on gloss annotations or a separate gloss-to-pose stage, the method fine-tunes an LLM to map spoken text directly to VQ-VAE pose tokens, with the help of stepwise-prompted "assistance" sequences that encode sign-language word order and relative motion duration. On Phoenix14T and How2Sign, the approach lowers DTW-MJE by 18.7% and 20.9% relative to Progressive Transformer and improves BLEU back-translation scores. The paper argues this is the first application of an off-the-shelf LLM to the production task of sign language.

What carries the argument

The central object is the assistance sequence: a text-format list, produced by prompting GPT-4o, that gives the important words of the input sentence in sign-language order and their relative motion durations, with the shortest word's duration set to 1. The assistance sequence is concatenated with the source sentence and fed into a decoder-only LLM, fine-tuned with LoRA, which autoregressively predicts VQ-VAE indices representing sign pose frames. The assistance does the work of aligning spoken-language word order with sign-language grammar and providing temporal cues, while a frozen pose decoder turns the predicted tokens back into joint poses.

What would settle it

Retrain with the assistance sequence replaced by permuted word order or uniform durations and compare DTW-MJE and BLEU; if scores stay at the with-assistance level, the stepwise prompting is not the source of the gain.

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Extended reading notes

Core claim

The central claim is that a language-only LLM can bridge the distribution gap between spoken-language text and sign pose tokens, provided it is given intermediate assistance sequences that spell out sign-language sentence structure and the relative duration of each word's motion. These sequences are generated by prompting GPT-4o in a stepwise fashion, then fed alongside the text and the VQ-VAE-quantized pose tokens during LoRA fine-tuning of LLaMA3-8B or Qwen2-7B. With assistance, the reported DTW-MJE reaches 0.1056 on Phoenix14T and 0.1371 on How2Sign, compared with 0.1276 and 0.1733 for Progressive Transformer, and the gains exceed the same models trained without assistance. The paper presents this as evidence that an LLM's inherent sign-language knowledge and reasoning ability can be exploited to generate sign language videos directly from text.

Load-bearing premise

The method assumes the assistant LLM's stepwise answers about sign-language sentence structure and relative word durations are accurate and consistent enough to teach the fine-tuned model; the paper does not validate this against human sign-language knowledge.

Editorial extensions

If this is right

  • Sign language production no longer has to rely on gloss annotations or a separate gloss-to-pose stage; one fine-tuned LLM can map text to sign pose tokens directly.
  • The same pipeline works across languages by changing the prompt's language name, as demonstrated on German Sign Language (Phoenix14T) and American Sign Language (How2Sign).
  • The assistance sequences help more on the smaller Phoenix14T dataset, indicating that LLM prior knowledge can partially compensate for scarce sign-language training data.
  • The evaluation protocol, combining a retrained back-translation model with DTW-MJE and BLEU scores, offers a consistent way to compare future sign language production models.

Reading between the lines

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

  • An implication the paper leaves implicit: the method's ceiling is set by the assistant model's sign-language competence, so a stronger or sign-specialized assistant could improve results without changing the pipeline.
  • A testable extension would replace the hand-written stepwise prompts with human-verified sign-language grammar annotations, separating the contribution of prompt wording from the LLM's pretrained knowledge.
  • The repetition loss used to keep fine-grained pose tokens from collapsing into the same codebook entry is a generalizable idea that could transfer to other fine-grained motion-generation tasks.
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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

4 major / 4 minor

Summary. The paper proposes TEAM-Sign, a sign language production method that first tokenizes pose sequences with a VQ-VAE and then fine-tunes an off-the-shelf LLM (LLaMA3-8B or Qwen2-7B) with LoRA to autoregressively predict pose tokens from input text. Its main novelty is a 'stepwise prompting' stage in which GPT-4o is asked to produce an auxiliary sequence containing the important words, their sign-language order, and relative durations; this sequence is fed to the LLM as additional context during fine-tuning and inference. Experiments on Phoenix14T and How2Sign compare the method with Progressive Transformer and a w/o-assistance ablation using DTW-MJE and back-translation BLEU, reporting relative improvements of 18.7% and 20.9% and claiming to be the first use of an off-the-shelf LLM for sign language production.

Significance. If the auxiliary-signal mechanism is validated, the paper would make a useful contribution by showing that LLMs' pretrained text knowledge can be distilled into structured conditioning for sign language production without extra manual annotation. The manuscript has several strengths: it evaluates two LLM backbones on two standard benchmarks, includes a no-assistance ablation, and describes the VQ-VAE+LLM pipeline clearly enough to follow. However, the core evidence for the assistance mechanism is missing, the evaluation is limited to a single 2020 baseline, and no variance or significance testing is reported. The significance of the claimed result therefore depends on additional validation that is not currently in the paper.

major comments (4)
  1. [Sec. 3.2] The stepwise-prompted assistance is the core novelty, but its content is never validated. The three GPT-4o prompts ask for sign-language sentence structure, a sign-language sentence for the input text, and relative motion durations, yet the manuscript gives no example of an assistance sequence, no human or gold-standard comparison for ASL/DGS word order, and no consistency analysis across repeated prompts. Because the system prompt tells the fine-tuned LLM that the assistance encodes 'the important words, their supposed order and relative duration' (Sec. 3.2), any error in the generated assistance directly corrupts the training signal; the w/o-assistance rows in Tables 1-2 show the model still works without assistance, so the contribution rests on unverified auxiliary information. The authors should show sample assistance sequences, check them against sign-language linguistics or existing sign annotations, and analyze prompt sensitivity and repeatability.
  2. [Tables 1 and 2] The incremental gain from assistance is small and not statistically supported. On Phoenix14T, LLaMA3 improves from 0.1204 to 0.1056 DTW-MJE and Qwen2 from 0.1051 to 0.1038; on How2Sign, LLaMA3 improves from 0.1406 to 0.1371 and Qwen2 from 0.1422 to 0.1397. These differences are single runs with no seeds, error bars, or significance tests. Given the small margins, the claim that stepwise prompting extracts useful sign-language knowledge is not established; the gain could be noise, or it could come from any extra conditioning text rather than from linguistically correct ASL/DGS structure. Multiple seeds with variance and a significance or paired-observation test are needed.
  3. [Sec. 4.1] The evaluation only compares against Progressive Transformer from 2020, despite the Related Work citing more recent direct SLP methods such as T2S-GPT [7] and Neural Sign Actors [6]. Moreover, the w/o-assistance variant already outperforms Progressive Transformer, so the headline '18.7%' and '20.9%' improvements do not establish the stated superiority of the assistance mechanism. The back-translation evaluator is also retrained from Zhou et al. [2] on keypoint data without reporting its translation accuracy or the exact training/evaluation split, making the BLEU scores incomparable to previously published numbers. Recent baselines and a documented back-translation model are required.
  4. [Sec. 3 and Sec. 4.1] The experimental setup is under-specified. The paper does not report codebook size, token sequence length, LoRA rank, learning rate, number of training steps, optimizer, or hardware, and the VQ-VAE weights α, β, γ in Eqs. (3)-(4) are never given. Without these details, the empirical results cannot be reproduced or compared fairly with future work. A complete implementation section or supplementary hyperparameter table should be added.
minor comments (4)
  1. [Abstract and Contributions] The headline improvements are computed from different variants: 18.7% corresponds to Qwen2 on Phoenix14T, while 20.9% corresponds to LLaMA3 on How2Sign; the paper should state exactly which model and metric each relative gain refers to.
  2. [References] In Sec. 2, reference [21] is cited as the source for HamNoSys, but [21] is Walsh et al., 'Changing the representation,' which is not the original HamNoSys notation paper; the citation should be corrected to the proper HamNoSys reference.
  3. [Eq. (4)] The notation for kuniq and kfreq in Eq. (4) is not formally defined; the text should state that kuniq is the number of distinct codebook indices in the sequence and kfreq is the maximum count of any single index, and the spacing in 'kuniqand kfreqrepresent' should be fixed.
  4. [Fig. 1] The assistance example in Fig. 1, '[yesterday (3), store (2), I (1), went(2)],' should be explained in the caption or text: the notation is not defined until the system prompt in Sec. 3.2, and it is unclear whether the numbers denote order or duration.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the assistance sequence is an extra conditioning input, not a fitted or redefined target, and the pose-token supervision and held-out evaluation are real data.

full rationale

The paper's derivation chain is not circular under the stated criteria. In Section 3.2, GPT-4o is prompted to generate an 'assistance' sequence (sign language sentence structure and relative word durations), and this sequence is fed as conditioning text into LLaMA-3 or Qwen2 during fine-tuning. The assistance is neither fitted to the target pose-token sequences nor derived from them; it is an independent text input produced by a separate off-the-shelf model. The supervised target is the sequence of VQ-VAE indices from real sign-language videos, optimized by cross-entropy loss in Eq. (6), and evaluation (DTW-MJE and back-translation BLEU) is performed on held-out test data. No equation in the paper reduces to its own input by construction, and no fitted parameter is renamed as a prediction. The fact that the 'w/o assistance' ablations already beat Progressive Transformer weakens the causal attribution of gains to the assistance, but that is an empirical-support concern, not circularity. The only self-citation, reference [1] in the introduction, is used merely as a background survey for progress in sign-language translation and is not load-bearing; no uniqueness theorem, ansatz, or defining assumption is imported from the authors' prior work. Accordingly, the central claim, while limited by lack of validation of GPT-4o assistance quality and absence of significance testing, is not circular.

Assumptions & free parameters 5 free parameters · 3 assumptions · 0 invented entities

No new physical or conceptual entities are introduced. The assistance sequence is a data structure, not an invented entity. The main unstated dependencies are the assumed reliability of LLM sign language knowledge, the validity of the retrained back-translation evaluator, and the fidelity of the VQ-VAE representation.

free parameters (5)
  • alpha
    Weight of the commitment loss in Eq. (3); value not reported in the paper, presumably tuned.
  • beta
    Weight of the uniqueness term in the repetition loss Eq. (4); value not reported.
  • gamma
    Weight of the frequency term in the repetition loss Eq. (4); value not reported.
  • VQ-VAE codebook size and feature dimensions
    Not specified; affects token granularity and the difficulty of the LLM mapping.
  • LoRA rank and learning rate
    Not specified; standard LoRA hyperparameters that influence fine-tuning behavior.
assumptions (3)
  • domain assumption Large language models contain reliable sign language knowledge in their training corpus
    Invoked in Sections 1 and 3.2 as the basis for extracting assistance sequences; no independent evidence provided.
  • domain assumption The back-translation model retrained from Zhou et al. [2] provides a valid measure of sign language production quality
    Used to compute BLEU scores in Tables 1-2; the model is not released and its accuracy is not characterized.
  • domain assumption VQ-VAE with the repetition loss faithfully represents sign language pose sequences
    The pose encoder and decoder are trained only for reconstruction; the paper admits finger details are lost, yet the token sequences are treated as the language the LLM must learn.

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Cite this review

Pith. "Pith review of Teach Me Sign: Stepwise Prompting LLM for Sign Language Production." pith.science (2026). https://pith.science/paper/XOB7L3H5

@misc{pith2026250710972,
  author       = {Pith},
  title        = {Pith review of: Teach Me Sign: Stepwise Prompting LLM for Sign Language Production},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XOB7L3H5}},
  note         = {Machine review of arXiv:2507.10972}
}
read the original abstract

Large language models, with their strong reasoning ability and rich knowledge, have brought revolution to many tasks of AI, but their impact on sign language generation remains limited due to its complexity and unique rules. In this paper, we propose TEAch Me Sign (TEAM-Sign), treating sign language as another natural language. By fine-tuning an LLM, we enable it to learn the correspondence between text and sign language, and facilitate generation. Considering the differences between sign and spoken language, we employ a stepwise prompting strategy to extract the inherent sign language knowledge within the LLM, thereby supporting the learning and generation process. Experimental results on How2Sign and Phoenix14T datasets demonstrate that our approach effectively leverages both the sign language knowledge and reasoning capabilities of LLM to align the different distribution and grammatical rules between sign and spoken language.

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Reference graph

Works this paper leans on

35 extracted references · 26 canonical work pages

  1. [7]

    Gloss-free sign language translation: Improving from visual-language pretraining,

    Benjia Zhou, Zhigang Chen, Albert Clap ´es, Jun Wan, Yanyan Liang, Sergio Escalera, Zhen Lei, and Du Zhang, “Gloss-free sign language translation: Improving from visual-language pretraining,” in ICCV, 2023, pp. 20871–20881

    work page 2023

  2. [6]

    A review of deep learning-based approaches to sign language processing,

    Sihan Tan, Nabeela Khan, Zhaoyi An, Yoshitaka Ando, Rei Kawakami, and Kazuhiro Nakadai, “A review of deep learning-based approaches to sign language processing,” Ad- vanced Robotics, pp. 1–19, 2024

    work page 2024

  3. [2]

    Two-stage approaches mainly rely on additional anno- tated information, such as gloss, to provide extra supervision during the generation phase

    RELA TED WORK Sign Language Production Beside traditional SLP meth- ods [18, 19], deep learning-based ones, as explained above, are basically divided into two types, two-stage approaches and direct approaches [1]. Two-stage approaches mainly rely on additional anno- tated information, such as gloss, to provide extra supervision during the generation phase...

    work page

  4. [1]

    Teach Me Sign: Stepwise Prompting LLM for Sign Language Production

    INTRODUCTION Given textual input, sign language production aims to produce corresponding sign language videos for the hearing-impaired community to understand. As an important part to facilitate seamless communication with the community, this task is of great significance in helping them integrate into society, en- hancing social inclusion and understandi...

    work page Pith review arXiv 2025

  5. [3]

    language

    PROPOSED METHOD Our proposed TEAM-Sign, as shown in Fig 1, leverages the embedded sign language knowledge and reasoning capabili- ties of LLMs to facilitate the generation from text to sign lan- guage video. Through the sign language encoding stage, we address the modality gap between sign language videos and texts by converting the sign video into a sequ...

    work page 1905

  6. [4]

    EXPERIMENTS To verify our proposed TEAM-Sign, we conducted com- parison experiments on two sign language datasets, namely Phoenix14T [29] and How2Sign [30]. As one of the most widely used datasets in the field of sign language recognition and generation, Phoenix14T contains 8,257 German sen- tences along with their corresponding sign language videos, with...

    work page

  7. [5]

    Experimental results on the Phoenix14T and How2Sign datasets verify the superior- ity of our TEAM-Sign method

    CONCLUSION In this paper, we propose TEAM-Sign, which is, to the best of our knowledge, the first model to utilize LLM’s sign language knowledge and reasoning ability simultaneously to perform the sign language production task. Experimental results on the Phoenix14T and How2Sign datasets verify the superior- ity of our TEAM-Sign method. Additionally, the ...

    work page

  8. [8]

    Progressive transformers for end-to-end sign language pro- duction,

    Ben Saunders, Necati Cihan Camgoz, and Richard Bowden, “Progressive transformers for end-to-end sign language pro- duction,” in ECCV. Springer, 2020, pp. 687–705

    work page 2020

Show all 35 references
  1. [9]

    Dualsign: Semi-supervised sign language production with balanced multi-modal multi-task dual transformation,

    Wencan Huang, Zhou Zhao, Jinzheng He, and Mingmin Zhang, “Dualsign: Semi-supervised sign language production with balanced multi-modal multi-task dual transformation,” in ACM MM, 2022, pp. 5486–5495

    2022

  2. [10]

    Autoregressive sign language production: A gloss-free approach with discrete representations,

    Eui Jun Hwang, Huije Lee, and Jong C Park, “Autoregressive sign language production: A gloss-free approach with discrete representations,” arXiv preprint arXiv:2309.12179, 2023

    2023 arXiv

  3. [11]

    Neural sign actors: A diffusion model for 3d sign language production from text,

    Vasileios Baltatzis, Rolandos Alexandros Potamias, Evange- los Ververas, Guanxiong Sun, Jiankang Deng, and Stefanos Zafeiriou, “Neural sign actors: A diffusion model for 3d sign language production from text,” in CVPR, 2024, pp. 1985– 1995

    2024

  4. [12]

    T2s-gpt: Dynamic vector quantization for autoregressive sign language production from text,

    Aoxiong Yin, Haoyuan Li, Kai Shen, Siliang Tang, and Yuet- ing Zhuang, “T2s-gpt: Dynamic vector quantization for autoregressive sign language production from text,” arXiv preprint arXiv:2406.07119, 2024

    2024 arXiv

  5. [13]

    Llama: Open and efficient foundation language models,

    Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timoth ´ee Lacroix, Baptiste Rozi`ere, Naman Goyal, Eric Hambro, Faisal Azhar, et al., “Llama: Open and efficient foundation language models,” arXiv preprint arXiv:2302.13971, 2023

    2023 arXiv

  6. [14]

    Lan- guage models are few-shot learners,

    Tom Brown, Benjamin Mann, Nick Ryder, Melanie Sub- biah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al., “Lan- guage models are few-shot learners,” NeurIPS, vol. 33, pp. 1877–1901, 2020

    1901

  7. [15]

    Visual instruction tuning,

    Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee, “Visual instruction tuning,” in NeurIPS, 2023

    2023

  8. [16]

    Bliva: A simple multimodal llm for better han- dling of text-rich visual questions,

    Wenbo Hu, Yifan Xu, Yi Li, Weiyue Li, Zeyuan Chen, and Zhuowen Tu, “Bliva: A simple multimodal llm for better han- dling of text-rich visual questions,” in AAAI, 2024, vol. 38, pp. 2256–2264

    2024

  9. [17]

    Sinc: Spatial composition of 3d human motions for simultaneous action generation,

    Nikos Athanasiou, Mathis Petrovich, Michael J Black, and G¨ul Varol, “Sinc: Spatial composition of 3d human motions for simultaneous action generation,” in ICCV, 2023, pp. 9984– 9995

    2023

  10. [18]

    Free-bloom: Zero-shot text-to-video gener- ator with llm director and ldm animator,

    Hanzhuo Huang, Yufan Feng, Cheng Shi, Lan Xu, Jingyi Yu, and Sibei Yang, “Free-bloom: Zero-shot text-to-video gener- ator with llm director and ldm animator,” NeurIPS, vol. 36, 2024

    2024

  11. [19]

    Motiongpt: Finetuned llms are general-purpose motion gen- erators,

    Yaqi Zhang, Di Huang, Bin Liu, Shixiang Tang, Yan Lu, Lu Chen, Lei Bai, Qi Chu, Nenghai Yu, and Wanli Ouyang, “Motiongpt: Finetuned llms are general-purpose motion gen- erators,” in AAAI, 2024, vol. 38, pp. 7368–7376

    2024

  12. [20]

    LLM-grounded video diffusion models,

    Long Lian, Baifeng Shi, Adam Yala, Trevor Darrell, and Boyi Li, “LLM-grounded video diffusion models,” in ICLR, 2024

    2024

  13. [21]

    Vision language models are blind,

    Pooyan Rahmanzadehgervi, Logan Bolton, Mohammad Reza Taesiri, and Anh Totti Nguyen, “Vision language models are blind,” arXiv preprint arXiv:2407.06581, 2024

    2024 arXiv

  14. [22]

    Neural discrete representation learning,

    Aaron Van Den Oord, Oriol Vinyals, et al., “Neural discrete representation learning,” NeurIPS, vol. 30, 2017

    2017

  15. [23]

    Sign lan- guage avatars: Animation and comprehensibility,

    Michael Kipp, Alexis Heloir, and Quan Nguyen, “Sign lan- guage avatars: Animation and comprehensibility,” in Intelli- gent Virtual Agents: 10th International Conference . Springer, 2011, pp. 113–126

    2011

  16. [24]

    An automated technique for real-time production of lifelike ani- mations of american sign language,

    John McDonald, Rosalee Wolfe, Jerry Schnepp, et al., “An automated technique for real-time production of lifelike ani- mations of american sign language,” Universal Access in the Information Society, vol. 15, pp. 551–566, 2016

    2016

  17. [25]

    Ham2pose: Animating sign language notation into pose se- quences,

    Rotem Shalev Arkushin, Amit Moryossef, and Ohad Fried, “Ham2pose: Animating sign language notation into pose se- quences,” in CVPR, 2023, pp. 21046–21056

    2023

  18. [26]

    Chang- ing the representation: Examining language representa- tion for neural sign language production,

    Harry Walsh, Ben Saunders, and Richard Bowden, “Chang- ing the representation: Examining language representa- tion for neural sign language production,” arXiv preprint arXiv:2210.06312, 2022

    2022 arXiv

  19. [27]

    Bert: Pre-training of deep bidirectional transformers for language understanding,

    Jacob Devlin, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805, 2018

    2018 arXiv

  20. [28]

    Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehen- sion,

    M Lewis, “Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehen- sion,” arXiv preprint arXiv:1910.13461, 2019

    1910 arXiv

  21. [29]

    Motiongpt: Human motion as a foreign language,

    Biao Jiang, Xin Chen, Wen Liu, Jingyi Yu, Gang Yu, and Tao Chen, “Motiongpt: Human motion as a foreign language,” NeurIPS, vol. 36, 2024

    2024

  22. [30]

    Gpt-4o system card,

    Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, et al., “Gpt-4o system card,” arXiv preprint arXiv:2410.21276, 2024

    2024 arXiv

  23. [31]

    The llama 3 herd of models,

    Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Ab- hishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al., “The llama 3 herd of models,” arXiv preprint arXiv:2407.21783 , 2024

    2024 arXiv

  24. [32]

    Qwen2 techni- cal report,

    Yang An, Baosong Yang, Binyuan Hui, et al., “Qwen2 techni- cal report,” arXiv preprint arXiv:2407.10671, 2024

    2024 arXiv

  25. [33]

    Lora: Low-rank adaptation of large language models,

    Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen, “Lora: Low-rank adaptation of large language models,”arXiv preprint arXiv:2106.09685, 2021

    2021 arXiv

  26. [34]

    Neural sign language trans- lation,

    Necati Cihan Camgoz, Simon Hadfield, Oscar Koller, Her- mann Ney, and Richard Bowden, “Neural sign language trans- lation,” in CVPR, 2018, pp. 7784–7793

    2018

  27. [35]

    How2sign: a large-scale multimodal dataset for continuous american sign language,

    Amanda Duarte, Shruti Palaskar, Lucas Ventura, Deepti Ghadi- yaram, Kenneth DeHaan, Florian Metze, Jordi Torres, and Xavier Giro-i Nieto, “How2sign: a large-scale multimodal dataset for continuous american sign language,” in CVPR, 2021, pp. 2735–2744

    2021

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