Papers with 0.3B
BioHiCL: Hierarchical Multi-Label Contrastive Learning for Biomedical Retrieval with MeSH Labels (2026.acl-short)
Copied to clipboard
| Challenge: | Existing biomedical generative retrievers lack domain semantics and hierarchical relationships among biomedically related texts. |
| Approach: | They propose a biomedical retrieval model with hierarchical multi-label contrastive learning that leverages hierarchic MeSH annotations to provide structured supervision for multi-labor contrastive training. |
| Outcome: | The proposed models achieve promising performance on biomedical retrieval, sentence similarity, and question answering tasks while remaining computationally efficient for deployment. |
On Bilingual Lexicon Induction with Large Language Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to induction bilingual lexicons still require cross-lingual word representations . a recent study shows that few-shot prompting with in-context examples from nearest neighbours achieves the best performance . |
| Approach: | They examine whether it is possible to prompt and fine-tune multilingual LLMs for BLI . they experiment with 18 open-source text-to-text mLLMs of different sizes . |
| Outcome: | The proposed approach is compared with existing approaches on two standard BLI benchmarks covering a range of typologically diverse languages. |