Teach Me Sign: Stepwise Prompting LLM for Sign Language Production

• URL: http://arxiv.org/abs/2507.10972v1
• Date: Tue, 15 Jul 2025 04:31:52 GMT
• Title: Teach Me Sign: Stepwise Prompting LLM for Sign Language Production
• Authors: Zhaoyi An, Rei Kawakami,
• Abstract summary: We propose TEAch Me Sign (TEAM-Sign), treating sign language as another natural language.<n>By fine-tuning an LLM, we enable it to learn the correspondence between text and sign language.<n>Considering the differences between sign and spoken language, we employ a stepwise prompting strategy to extract the inherent sign language knowledge.
• Score: 4.855031479710184
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Signs as Tokens: A Retrieval-Enhanced Multilingual Sign Language Generator [55.94334001112357]
  We introduce a multilingual sign language model, Signs as Tokens (SOKE), which can generate 3D sign avatars autoregressively from text inputs.<n>We propose a retrieval-enhanced SLG approach, which incorporates external sign dictionaries to provide accurate word-level signs.
  arXiv  Detail & Related papers  (2024-11-26T18:28:09Z)
• Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation [6.688680877428467]
  We propose a novel gloss-free Multimodal Sign Language Translation framework. We generate detailed textual descriptions of sign language components using multimodal large language models. Our approach achieves state-of-the-art performance on benchmark datasets PHOENIX14T and CSL-Daily.
  arXiv  Detail & Related papers  (2024-11-25T09:01:41Z)
• SHuBERT: Self-Supervised Sign Language Representation Learning via Multi-Stream Cluster Prediction [65.1590372072555]
  SHuBERT (Sign Hidden-Unit BERT) is a self-supervised contextual representation model learned from 1,000 hours of American Sign Language video.<n>SHuBERT adapts masked token prediction objectives to multi-stream visual sign language input, learning to predict multiple targets corresponding to clustered hand, face, and body pose streams.<n>SHuBERT achieves state-of-the-art performance across multiple tasks including sign language translation, isolated sign language recognition, and fingerspelling detection.
  arXiv  Detail & Related papers  (2024-11-25T03:13:08Z)
• Enhanced Sign Language Translation between American Sign Language (ASL) and Indian Sign Language (ISL) Using LLMs [0.2678472239880052]
  We have come up with a research that hopes to provide a bridge between the users of American Sign Language and the users of spoken language and Indian Sign Language (ISL) This framework is tasked with key challenges such as automatically dealing with gesture variability and overcoming the linguistic differences between ASL and ISL.
  arXiv  Detail & Related papers  (2024-11-19T17:45:12Z)
• Scaling up Multimodal Pre-training for Sign Language Understanding [96.17753464544604]
  Sign language serves as the primary meaning of communication for the deaf-mute community. To facilitate communication between the deaf-mute and hearing people, a series of sign language understanding (SLU) tasks have been studied. These tasks investigate sign language topics from diverse perspectives and raise challenges in learning effective representation of sign language videos.
  arXiv  Detail & Related papers  (2024-08-16T06:04:25Z)
• MS2SL: Multimodal Spoken Data-Driven Continuous Sign Language Production [93.32354378820648]
  We propose a unified framework for continuous sign language production, easing communication between sign and non-sign language users. A sequence diffusion model, utilizing embeddings extracted from text or speech, is crafted to generate sign predictions step by step. Experiments on How2Sign and PHOENIX14T datasets demonstrate that our model achieves competitive performance in sign language production.
  arXiv  Detail & Related papers  (2024-07-04T13:53:50Z)
• Using an LLM to Turn Sign Spottings into Spoken Language Sentences [30.882890874888417]
  Sign Language Translation (SLT) is a challenging task that aims to generate spoken language sentences from sign language videos. In this paper, we introduce a hybrid SLT approach, Spotter+GPT, that utilizes a sign spotter and a powerful Large Language Model (LLM) to improve SLT performance.
  arXiv  Detail & Related papers  (2024-03-15T16:14:34Z)
• Learnt Contrastive Concept Embeddings for Sign Recognition [33.72708697077754]
  We focus on explicitly creating sign embeddings that bridge the gap between sign language and spoken language. We train a vocabulary of embeddings that are based on the linguistic labels for sign video. We develop a conceptual similarity loss which is able to utilise word embeddings from NLP methods to create sign embeddings that have better sign language to spoken language correspondence.
  arXiv  Detail & Related papers  (2023-08-18T12:47:18Z)
• All You Need In Sign Language Production [50.3955314892191]
  Sign language recognition and production need to cope with some critical challenges. We present an introduction to the Deaf culture, Deaf centers, psychological perspective of sign language. Also, the backbone architectures and methods in SLP are briefly introduced and the proposed taxonomy on SLP is presented.
  arXiv  Detail & Related papers  (2022-01-05T13:45:09Z)
• Including Signed Languages in Natural Language Processing [48.62744923724317]
  Signed languages are the primary means of communication for many deaf and hard of hearing individuals. This position paper calls on the NLP community to include signed languages as a research area with high social and scientific impact.
  arXiv  Detail & Related papers  (2021-05-11T17:37:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.