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Large language models (LLMs) have demonstrated an impressive ability to perform arithmetic and symbolic reasoning tasks, when provided with a few examples at test time ("few-shot prompting"). Much of this success can be attributed to prompting methods such as "chain-of-thought", which employ LLMs for both understanding the problem description by decomposing it into steps, as well as solving each step of the problem. While LLMs seem to be adept at this sort of step-by-step decomposition, LLMs often make logical and arithmetic mistakes in the solution part, even when the problem is decomposed correctly. In this paper, we present Program-Aided Language models (PAL): a novel approach that uses the LLM to read natural language problems and generate programs as the intermediate reasoning steps, but offloads the solution step to a runtime such as a Python interpreter. With PAL, decomposing the natural language problem into runnable steps remains the only learning task for the LLM, while solving is delegated to the interpreter. We demonstrate this synergy between a neural LLM and a symbolic interpreter across 13 mathematical, symbolic, and algorithmic reasoning tasks from BIG-Bench Hard and others. In all these natural language reasoning tasks, generating code using an LLM and reasoning using a Python interpreter leads to more accurate results than much larger models. For example, PAL using Codex achieves state-of-the-art few-shot accuracy on GSM8K, surpassing PaLM which uses chain-of-thought by absolute 15% top-1.
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This content will become publicly available on November 13, 2025
Universal Sign Language Recognition System Using Gesture Description Generation and Large Language Model
Sign language is a priceless means of communication for deaf and hard-of-hearing people to fully enable them to participate in society and interact with others. This study introduces a novel universal sign language system that uses the Gesture-script to generate a detailed description of gestures in videos, which involve continuous movement of hands, arms, heads, and body language. Subsequently, we input this description into a Large Language Model (LLM) to interpret sign language. We deployed a few-shot prompting technique for LLM, enabling it to precisely transfer the sign videos into corresponding sentences in natural language. Furthermore, the Few-shot prompting technique enables our system to interpret multiple types of sign language without pre-training or fine-tuning.
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- Award ID(s):
- 2245607
- PAR ID:
- 10598017
- Publisher / Repository:
- Springer Nature Switzerland
- Date Published:
- ISBN:
- 978-3-031-71469-6
- Page Range / eLocation ID:
- 279 to 289
- Subject(s) / Keyword(s):
- Sign language interpretation, Large Language Model(LLM), Masked Auto-encoder(MAE), Few-shot prompting · Gesture
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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