What question did this study set out to answer?

This research aims to enhance abbreviation disambiguation performance in large language models by developing the IN-LAND framework.

IN-LAND: Improving large language models’ performance on abbreviation disambiguation

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Randomized trial evaluates the IN-LAND framework for abbreviation disambiguation in various languages, indicating notable improvements.

This research aims to enhance abbreviation disambiguation performance in large language models by developing the IN-LAND framework.
Developed the IN-LAND framework for abbreviation disambiguation using a novel selection mechanism for demonstration examples.
Evaluated performance across four large language models in languages like French, Legal English, and Malay.
Compared IN-LAND results to a Random K-shot in-context learning baseline.
In Malay, average macro F1 score improved by 7.11% and accuracy by 9.19%.
In French, the macro F1 score increased by 2.69% and accuracy by 3.17%.
Legal English showed a 1.04% improvement in average accuracy while maintaining competitive macro F1 scores.

Lin et al. (2026) studied this question.

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View Full Paper

Ask AI

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Authors

Randomized trial evaluates the IN-LAND framework for abbreviation disambiguation in various languages, indicating notable improvements.

This research aims to enhance abbreviation disambiguation performance in large language models by developing the IN-LAND framework.
Developed the IN-LAND framework for abbreviation disambiguation using a novel selection mechanism for demonstration examples.
Evaluated performance across four large language models in languages like French, Legal English, and Malay.
Compared IN-LAND results to a Random K-shot in-context learning baseline.
In Malay, average macro F1 score improved by 7.11% and accuracy by 9.19%.
In French, the macro F1 score increased by 2.69% and accuracy by 3.17%.
Legal English showed a 1.04% improvement in average accuracy while maintaining competitive macro F1 scores.

Lin et al. (2026) studied this question.

View Full Paper

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