Instruction Tuning is a fine-tuning process for large language models that uses natural language instructions to improve alignment with user intent. NEC Labs America applies instruction tuning to enhance model performance in biomedical text mining, multimodal document analysis, and network optimization. Instruction Tuning makes AI systems more responsive, transparent, and trustworthy, ensuring they meet real-world needs in applied domains.

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TSLA: Unified Time Series and Language Model

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Real-world time series data often require analysis or interpretation from domain experts. Some tasks, like time series question answering, involve both time series and natural language questions, posing challenges for single-modality language models to understand their interaction. To this end, we present TSLA (Time Series Language Model), a framework designed to enhance the language model with the understanding of time series data for multi-modality tasks. TSLA comprises three key components. (1) Time Series Tokenizer learns how to represent time series data into discrete tokens, making it more manageable for language models. (2) Joint (Pre-)Training on task-agnostic time series and text data integrates time series tokens and text tokens to model the interplay between time series and language concepts. (3) Multi-task Instruction Tuning fine-tunes the pretrained TSLA for various downstream tasks relevant to user interests. For evaluation, we applied TSLA to time series data from human motions on four tasks: time series captioning, time series question answering, text-based time series synthesis, and text-based time series continuation. The results demonstrate TSLA’s effectiveness in handling multiple time series analysis tasks, pointing the way for future research endeavors.

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