Combining Distantly Supervised Models with In Context Learning for Monolingual and Cross-Lingual Relation Extraction (2026.acl-long)
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| Challenge: | Existing Distantly Supervised Relation Extraction models rely on task-specific training, but their integration with in-context learning (ICL) using large language models (LLMs) remains underexplored. |
| Approach: | They propose a framework for distantly supervised relation extraction that uses a trained DSRE model to identify the top-k candidate relations for a given test sentence and a dynamic exemplar retrieval strategy that extracts reliable, sentence-level exemplars from training data. |
| Outcome: | The proposed framework achieves 20 F1 points gains in English and 17 F1 point gains on Indic languages over previous models and naive prompting baselines. |
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