# Results of the seventh edition of the BioASQ Challenge

arXiv: https://arxiv.org/abs/2006.09174 · HTML: https://mlsanity.com/paper/2006.09174/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2020.

## Built on by (most cited first, 28 of 28)

- [MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering](https://mlsanity.com/paper/2203.14371/) (2022)
- [BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining](https://mlsanity.com/paper/2210.10341/) (2022)
- [LinkBERT: Pretraining Language Models with Document Links](https://mlsanity.com/paper/2203.15827/) (2022)
- [Deep Bidirectional Language-Knowledge Graph Pretraining](https://mlsanity.com/paper/2210.09338/) (2022)
- [Pre-trained Language Models in Biomedical Domain: A Systematic Survey](https://mlsanity.com/paper/2110.05006/) (2021)
- [Fine-Tuning Large Neural Language Models for Biomedical Natural Language Processing](https://mlsanity.com/paper/2112.07869/) (2021)
- [nach0: Multimodal Natural and Chemical Languages Foundation Model](https://mlsanity.com/paper/2311.12410/) (2023)
- [LIQUID: A Framework for List Question Answering Dataset Generation](https://mlsanity.com/paper/2302.01691/) (2023)
- [Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A Survey](https://mlsanity.com/paper/2403.01528/) (2024)
- [Mixture-of-Partitions: Infusing Large Biomedical Knowledge Graphs into BERT](https://mlsanity.com/paper/2109.04810/) (2021)
- [Overview of BioASQ 2020: The eighth BioASQ challenge on Large-Scale Biomedical Semantic Indexing and Question Answering](https://mlsanity.com/paper/2106.14618/) (2021)
- [Sequence tagging for biomedical extractive question answering](https://mlsanity.com/paper/2104.07535/) (2021)
- [Improving Health Question Answering with Reliable and Time-Aware Evidence Retrieval](https://mlsanity.com/paper/2404.08359/) (2024)
- [BioMamba: Domain-Adaptive Biomedical Language Models](https://mlsanity.com/paper/2408.02600/) (2024)
- [What Would it Take to get Biomedical QA Systems into Practice?](https://mlsanity.com/paper/2109.10415/) (2021)
- [Explanatory Argument Extraction of Correct Answers in Resident Medical Exams](https://mlsanity.com/paper/2312.00567/) (2023)
- [THiFLY Research at SemEval-2023 Task 7: A Multi-granularity System for CTR-based Textual Entailment and Evidence Retrieval](https://mlsanity.com/paper/2306.01245/) (2023)
- [An Efficient Rehearsal Scheme for Catastrophic Forgetting Mitigation during Multi-stage Fine-tuning](https://mlsanity.com/paper/2402.08096/) (2024)
- [Low Resource Recognition and Linking of Biomedical Concepts from a Large Ontology](https://mlsanity.com/paper/2101.10587/) (2021)
- [UniDCP: Unifying Multiple Medical Vision-language Tasks via Dynamic Cross-modal Learnable Prompts](https://mlsanity.com/paper/2312.11171/) (2023)
- [Combining Evidence and Reasoning for Biomedical Fact-Checking](https://mlsanity.com/paper/2509.13879/) (2025)
- [Pre-training data selection for biomedical domain adaptation using journal impact metrics](https://mlsanity.com/paper/2409.02725/) (2024)
- [Science Checker: Extractive-Boolean Question Answering For Scientific Fact Checking](https://mlsanity.com/paper/2204.12263/) (2022)
- [Diversifying Knowledge Enhancement of Biomedical Language Models using Adapter Modules and Knowledge Graphs](https://mlsanity.com/paper/2312.13881/) (2023)
- [IITK at SemEval-2024 Task 2: Exploring the Capabilities of LLMs for Safe Biomedical Natural Language Inference for Clinical Trials](https://mlsanity.com/paper/2404.04510/) (2024)
- [Retrieval Augmented Biomedical Question Answering with Weak Question Recovery and Neural Reranking for BioASQ Task 14b](https://mlsanity.com/paper/2608.01468/) (2026)
- [Boosting Biomedical Concept Extraction by Rule-Based Data Augmentation](https://mlsanity.com/paper/2407.02719/) (2024)
- [Adapter-based Approaches to Knowledge-enhanced Language Models -- A Survey](https://mlsanity.com/paper/2411.16403/) (2024)

## Abstract (arXiv)

The results of the seventh edition of the BioASQ challenge are presented in this paper. The aim of the BioASQ challenge is the promotion of systems and methodologies through the organization of a challenge on the tasks of large-scale biomedical semantic indexing and question answering. In total, 30 teams with more than 100 systems participated in the challenge this year. As in previous years, the best systems were able to outperform the strong baselines. This suggests that state-of-the-art systems are continuously improving, pushing the frontier of research.