# Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation

arXiv: https://arxiv.org/abs/2411.16789 · HTML: https://mlsanity.com/paper/2411.16789/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 43 of its 67 references that are in the map.
- First posted on arXiv 25 Nov 2024.

## Builds on (most cited first, 30 of 43)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [Align before Fuse: Vision and Language Representation Learning with Momentum Distillation](https://mlsanity.com/paper/2107.07651/) (2021)
- [Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding](https://mlsanity.com/paper/2306.02858/) (2023)
- [Video-LLaVA: Learning United Visual Representation by Alignment Before Projection](https://mlsanity.com/paper/2311.10122/) (2023)
- [Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://mlsanity.com/paper/2212.03533/) (2022)
- [Kosmos-2: Grounding Multimodal Large Language Models to the World](https://mlsanity.com/paper/2306.14824/) (2023)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [SimVLM: Simple Visual Language Model Pretraining with Weak Supervision](https://mlsanity.com/paper/2108.10904/) (2021)
- [FILIP: Fine-grained Interactive Language-Image Pre-Training](https://mlsanity.com/paper/2111.07783/) (2021)
- [LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models](https://mlsanity.com/paper/2407.07895/) (2024)
- [SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities](https://mlsanity.com/paper/2305.11000/) (2023)
- [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://mlsanity.com/paper/2008.00401/) (2020)
- [Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation](https://mlsanity.com/paper/2003.13830/) (2020)
- [MultiModal-GPT: A Vision and Language Model for Dialogue with Humans](https://mlsanity.com/paper/2305.04790/) (2023)
- [Multimodal Large Language Models: A Survey](https://mlsanity.com/paper/2311.13165/) (2023)
- [Improving Sign Language Translation with Monolingual Data by Sign Back-Translation](https://mlsanity.com/paper/2105.12397/) (2021)
- [VLP: A Survey on Vision-Language Pre-training](https://mlsanity.com/paper/2202.09061/) (2022)
- [Two-Stream Network for Sign Language Recognition and Translation](https://mlsanity.com/paper/2211.01367/) (2022)
- [Gloss-free Sign Language Translation: Improving from Visual-Language Pretraining](https://mlsanity.com/paper/2307.14768/) (2023)

## Built on by (most cited first, 13 of 13)

- [Gloss-Free Sign Language Translation: An Unbiased Evaluation of Progress in the Field](https://mlsanity.com/paper/2603.13240/) (2026)
- [MaDiS: Taming Masked Diffusion Language Models for Sign Language Generation](https://mlsanity.com/paper/2601.19577/) (2026)
- [Large Sign Language Models: Toward 3D American Sign Language Translation](https://mlsanity.com/paper/2511.08535/) (2025)
- [Bangla Sign Language Translation: Dataset Creation Challenges, Benchmarking and Prospects](https://mlsanity.com/paper/2511.21533/) (2025)
- [SignAgent: Agentic LLMs for Linguistically-Grounded Sign Language Annotation and Dataset Curation](https://mlsanity.com/paper/2603.19059/) (2026)
- [SIGNET: Motion-Level Knowledge Transfer for Cross-Language Sign Language Translation](https://mlsanity.com/paper/2606.28626/) (2026)
- [Towards Multi-View Sign Language Understanding: A Benchmark Dataset and Baseline](https://mlsanity.com/paper/2604.18184/) (2026)
- [Direct Translation between Sign Languages](https://mlsanity.com/paper/2605.20588/) (2026)
- [SLU-2K: A Question-Based Benchmark for Semantic Evaluation of Sign Language Translation](https://mlsanity.com/paper/2606.03788/) (2026)
- [ViPo-MLLM: Visual-Pose Multimodal LLM for Gloss-Free Sign Language Translation](https://mlsanity.com/paper/2607.03657/) (2026)
- [Q-BridgeNet: A Quantization Network for Cross-Lingual Sign Language Translation](https://mlsanity.com/paper/2607.11215/) (2026)
- [SMART: MLLM-guided Temporal Alignment for Unifying Sign Language Recognition and Spotting](https://mlsanity.com/paper/2608.25493/) (2026)
- [Beyond BLEU: A Case for Redefining Sign Language Translation Benchmarks](https://mlsanity.com/paper/2609.03734/) (2026)

## Abstract (arXiv)

Sign language translation (SLT) is a challenging task that involves translating sign language images into spoken language. For SLT models to perform this task successfully, they must bridge the modality gap and identify subtle variations in sign language components to understand their meanings accurately. To address these challenges, we propose a novel gloss-free SLT framework called Multimodal Sign Language Translation (MMSLT), which leverages the representational capabilities of off-the-shelf multimodal large language models (MLLMs). Specifically, we use MLLMs to generate detailed textual descriptions of sign language components. Then, through our proposed multimodal-language pre-training module, we integrate these description features with sign video features to align them within the spoken sentence space. Our approach achieves state-of-the-art performance on benchmark datasets PHOENIX14T and CSL-Daily, highlighting the potential of MLLMs to be utilized effectively in SLT. Code is available at https://github.com/hwjeon98/MMSLT.