@inproceedings{kawarada-etal-2024-demonstration,
title = "Demonstration Selection Strategies for Numerical Time Series Data-to-Text",
author = "Kawarada, Masayuki and
Ishigaki, Tatsuya and
Topi{\'c}, Goran and
Takamura, Hiroya",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.435/",
doi = "10.18653/v1/2024.findings-emnlp.435",
pages = "7378--7392",
abstract = "Demonstration selection, the process of selecting examples used in prompts, plays a critical role in in-context learning. This paper explores demonstration selection methods for data-to-text tasks that involve numerical time series data as inputs.Previously developed demonstration selection methods primarily focus on textual inputs, often relying on embedding similarities of textual tokens to select similar instances from an example bank. However, this approach may not be suitable for numerical time series data.To address this issue, we propose two novel selection methods: (1) sequence similarity-based selection using various similarity measures, and (2) task-specific knowledge-based selection.From our experiments on two benchmark datasets, we found that our proposed models significantly outperform baseline selections and often surpass fine-tuned models. We also found that scale-invariant similarity measures such as Pearson`s correlation work better than scale-variant measures such as Euclidean distance.Manual evaluation by human judges also confirms that our proposed methods outperform conventional methods."
}
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<abstract>Demonstration selection, the process of selecting examples used in prompts, plays a critical role in in-context learning. This paper explores demonstration selection methods for data-to-text tasks that involve numerical time series data as inputs.Previously developed demonstration selection methods primarily focus on textual inputs, often relying on embedding similarities of textual tokens to select similar instances from an example bank. However, this approach may not be suitable for numerical time series data.To address this issue, we propose two novel selection methods: (1) sequence similarity-based selection using various similarity measures, and (2) task-specific knowledge-based selection.From our experiments on two benchmark datasets, we found that our proposed models significantly outperform baseline selections and often surpass fine-tuned models. We also found that scale-invariant similarity measures such as Pearson‘s correlation work better than scale-variant measures such as Euclidean distance.Manual evaluation by human judges also confirms that our proposed methods outperform conventional methods.</abstract>
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%0 Conference Proceedings
%T Demonstration Selection Strategies for Numerical Time Series Data-to-Text
%A Kawarada, Masayuki
%A Ishigaki, Tatsuya
%A Topić, Goran
%A Takamura, Hiroya
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F kawarada-etal-2024-demonstration
%X Demonstration selection, the process of selecting examples used in prompts, plays a critical role in in-context learning. This paper explores demonstration selection methods for data-to-text tasks that involve numerical time series data as inputs.Previously developed demonstration selection methods primarily focus on textual inputs, often relying on embedding similarities of textual tokens to select similar instances from an example bank. However, this approach may not be suitable for numerical time series data.To address this issue, we propose two novel selection methods: (1) sequence similarity-based selection using various similarity measures, and (2) task-specific knowledge-based selection.From our experiments on two benchmark datasets, we found that our proposed models significantly outperform baseline selections and often surpass fine-tuned models. We also found that scale-invariant similarity measures such as Pearson‘s correlation work better than scale-variant measures such as Euclidean distance.Manual evaluation by human judges also confirms that our proposed methods outperform conventional methods.
%R 10.18653/v1/2024.findings-emnlp.435
%U https://aclanthology.org/2024.findings-emnlp.435/
%U https://doi.org/10.18653/v1/2024.findings-emnlp.435
%P 7378-7392
Markdown (Informal)
[Demonstration Selection Strategies for Numerical Time Series Data-to-Text](https://aclanthology.org/2024.findings-emnlp.435/) (Kawarada et al., Findings 2024)
ACL