Using an LLM to Turn Sign Spottings into Spoken Language Sentences (2403.10434v2)

Abstract: 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 LLM to improve SLT performance. Spotter+GPT breaks down the SLT task into two stages. The videos are first processed by the Spotter, which is trained on a linguistic sign language dataset, to identify individual signs. These spotted signs are then passed to an LLM, which transforms them into coherent and contextually appropriate spoken language sentences. The source code of the Spotter is available at https://gitlab.surrey.ac.uk/cogvispublic/sign-spotter.

• The paper presents a novel prompt engineering approach using ChatGPT to generate coherent sentences from lists of sign language words.
• The refined prompt framework, with rules for handling incomplete inputs, significantly reduces the risk of unrelated outputs.
• This methodology offers practical insights for NLP applications in assistive technologies and automated translation systems.

Overview of "Using an LLM to Turn Signs into Speech"

The paper focuses on the methodology of using a LLM, specifically ChatGPT, to convert sign language inputs into spoken language. The authors detail the prompt engineering process they undertook to optimize the performance of ChatGPT in generating coherent sentences from a list of words. This involves developing an initial strategy and refining it through empirical observations.

Prompt Engineering Methodology

The paper begins with a basic prompt setup, aiming at generating sentences from a provided list of words. This rudimentary approach, while straightforward, exposed limitations of the LLM. Specifically, the model occasionally produced unrelated outputs, particularly in cases where sign recognition (gloss) was incomplete or nonexistent. To address this issue, the authors integrated additional rules into the prompt framework. These rules ensure that when translation is infeasible—due to the absence of detected signs or insufficient gloss data—the model responds with "No Translation" instead of unrelated content.

Implications

This research delineates a significant stride in improving LLM interaction through precise prompt engineering. By tailoring the prompt, the authors enhance the model's capacity to handle incomplete or ambiguous inputs. The implications extend to various applications in NLP, particularly in improving the robustness of LLMs in human-computer interaction scenarios. For example, this approach may benefit automated translation systems or assistive technologies for the hearing impaired.

Prospective Developments

The paper underlines the potential for further refinement of LLM applications through active prompt management. Future research could explore more sophisticated prompt-generation techniques, perhaps involving dynamic adaptation based on real-time feedback from LLMs. Additionally, there is room to extend this methodology to other languages and dialects, which broadens the scope of application domains.

In conclusion, while the paper provides a concentrated look at a niche application of LLMs, it prompts broader considerations for the alignment and control of these models to meet specific use-case requirements. This foundation could lead to more resilient and versatile AI systems, capable of seamless integration into diverse communicative contexts.