REVIEW 5 cited by
BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Developing effective biomedical retrieval models is important for excelling at knowledge-intensive biomedical tasks but still challenging due to the deficiency of sufficient publicly annotated biomedical data and computational resources. We present BMRetriever, a series of dense retrievers for enhancing biomedical retrieval via unsupervised pre-training on large biomedical corpora, followed by instruction fine-tuning on a combination of labeled datasets and synthetic pairs. Experiments on 5 biomedical tasks across 11 datasets verify BMRetriever's efficacy on various biomedical applications. BMRetriever also exhibits strong parameter efficiency, with the 410M variant outperforming baselines up to 11.7 times larger, and the 2B variant matching the performance of models with over 5B parameters. The training data and model checkpoints are released at \url{https://huggingface.co/BMRetriever} to ensure transparency, reproducibility, and application to new domains.
Forward citations
Cited by 5 Pith papers
-
Modular Representation Compression: Adapting LLMs for Efficient and Effective Recommendations
LLMs exhibit mid-layer representation advantage for recommendations; MARC compresses representations modularly to reduce costs while improving performance, as shown in a large-scale online advertising deployment.
-
Exploring the Capabilities of Large Language Model Encoders for Image-Text Retrieval in Chest X-rays
Domain-adapted LLM encoders trained with masked token prediction and supervised contrastive learning improve chest X-ray image-text retrieval and external generalization, reaching GREEN scores of 0.308 on MIMIC-CXR an...
-
Active Learning for Neurosymbolic Program Synthesis
The abstract claims a new active learning technique, constrained conformal evaluation (tool SmartLabel), that finds the ground-truth program in 98% of benchmarks, but the delivered full text is a different paper, leav...
-
R2MED: A Benchmark for Reasoning-Driven Medical Retrieval
R2MED is the first benchmark for reasoning-driven medical retrieval, where even top models reach only 41.4 nDCG@10 on queries requiring inference beyond lexical or semantic overlap.
-
A Multi-Task Evaluation of LLMs' Processing of Academic Text Input
The abstract reports Gemini underperforms on four academic text tasks, but the attached full text is an unrelated biomedical retrieval paper, leaving the claims unverifiable.
Discussion (0). Sign in to comment.