We lifted 4 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| toneli/petailor-for-bio-triple-extraction | canonical | 3 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| calclulate_f1 | Ran | toneli/petailor-for-bio-triple-extraction/4_generation_triple_model/0_F1_triplet_evalution.py pointer only (licence: NONE) · get_code("416adc8075a063ad") |
| cos_similisty | Ran | toneli/petailor-for-bio-triple-extraction/0_make_relation_chuck_and_scorer_data/2_make_similar_chuck_for_train_5.py pointer only (licence: NONE) · get_code("89629b110a6a8b55") |
| formatting_func | Ran | toneli/petailor-for-bio-triple-extraction/3_trainning_triple_model/trainer.py pointer only (licence: NONE) · get_code("82613ffccd396680") |
| prepare_data | Not yet run | toneli/petailor-for-bio-triple-extraction/3_trainning_triple_model/trainer.py pointer only (licence: NONE) · get_code("56babd0fe5ddc04c") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Large Language Models (LLMs) have swiftly emerged as vital resources for different applications in the biomedical and healthcare domains; however, these models encounter issues such as generating inaccurate information or hallucinations. Retrieval-augmented generation provided a solution for these models to update knowledge and enhance their performance. In contrast to previous retrieval-augmented LMs, which utilize specialized cross-attention mechanisms to help LLM encode retrieved text, BiomedRAG adopts a simpler approach by directly inputting the retrieved chunk-based documents into the LLM. This straightforward design is easily applicable to existing retrieval and language models, effectively bypassing noise information in retrieved documents, particularly in noise-intensive tasks. Moreover, we demonstrate the potential for utilizing the LLM to supervise the retrieval model in the biomedical domain, enabling it to retrieve the document that assists the LM in improving its predictions. Our experiments reveal that with the tuned scorer,\textsc{ BiomedRAG} attains superior performance across 5 biomedical NLP tasks, encompassing information extraction (triple extraction, relation extraction), text classification, link prediction, and question-answering, leveraging over 9 datasets. For instance, in the triple extraction task, \textsc{BiomedRAG} outperforms other triple extraction systems with micro-F1 scores of 81.42 and 88.83 on GIT and ChemProt corpora, respectively.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2405.00465")
get_code_for_paper("2405.00465")
have("2405.00465")
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