Challenge: Existing models of biomedical question answering are limited in their ability to predict answers . a new model improves the performance of existing models, but the code will be released after the paper is published.
Approach: They propose a hierarchical representation-based dynamic reasoning network to solve biomedical problems.
Outcome: The proposed model significantly improves on three mainstream biomedical datasets . the code will be released after the paper is published .

Similar Papers

DRLK: Dynamic Hierarchical Reasoning with Language Model and Knowledge Graph for Question Answering (2022.emnlp-main)

Copied to clipboard

Challenge: Existing work only uses the same QA context representation to interact with multiple layers of KG, which results in a restricted interaction.
Approach: They propose a model that utilizes dynamic hierarchical interactions between QA context and KG for reasoning.
Outcome: The proposed model performs state-of-the-art on two benchmark datasets and competitively on the others.
Hierarchical Graph Network for Multi-hop Question Answering (2020.emnlp-main)

Copied to clipboard

Challenge: Existing multi-hop question answering models focus on multi-level reasoning across multiple documents or paragraphs.
Approach: They propose a hierarchical graph network that aggregates clues from scattered texts . they use a set of contextual encoders to initialize nodes on different levels of granularity .
Outcome: The proposed model outperforms existing multi-hop QA approaches on the HotpotQA benchmark.
Reasoning or Knowledge: Stratified Evaluation of Biomedical LLMs (2026.eacl-long)

Copied to clipboard

Challenge: Medical reasoning in large language models is a complex cognitive process through which clinicians interpret patient data and make diagnostic and therapeutic decisions.
Approach: They propose an evaluation framework that disentangles knowledge recall from reasoning by training a PubMedBERT-based classifier and applying it to 11 widely used biomedical QA benchmarks.
Outcome: The proposed evaluation framework disentangles knowledge recall from reasoning by training a PubMedBERT-based classifier and applying it to 11 widely used biomedical QA benchmarks.
Semantic Graphs for Generating Deep Questions (2020.acl-main)

Copied to clipboard

Challenge: Existing research has focused on generating factoid questions relevant to one fact obtainable from a single sentence.
Approach: They propose a framework that first constructs a semantic-level graph and then encodes it by introducing an attention-based GGNN.
Outcome: The proposed framework captures the global structure of the document and facilitates reasoning over multiple facts.
Multi-Granularity Hierarchical Attention Fusion Networks for Reading Comprehension and Question Answering (P18-1)

Copied to clipboard

Challenge: Existing approaches to read comprehension style question answering are limited by the volume of annotated datasets.
Approach: They propose a hierarchical attention network for reading comprehension style question answering . they first encode the question and paragraph with fine-grained language embeddings . then propose fusion approach to fuse information from both global and attended representations based on the hierarchic attention network .
Outcome: The proposed method achieves state-of-the-art on the SQuAD and TriviaQA Wiki leaderboards and two adversarial SQu AD datasets.
MedREQAL: Examining Medical Knowledge Recall of Large Language Models via Question Answering (2024.findings-acl)

Copied to clipboard

Challenge: Large language models can encode knowledge during pre-training on large text corpora, enabling downstream tasks like question answering (QA).
Approach: They construct a dataset derived from systematic reviews to examine their ability to encode medical knowledge and their recall.
Outcome: The proposed model performs well on the biomedical QA dataset.
MedEx: Enhancing Medical Question-Answering with First-Order Logic based Reasoning and Knowledge Injection (2025.coling-main)

Copied to clipboard

Challenge: Existing knowledge triples are ineffective in medical question-answering because of superfluous data and inability to capture complex relationships between symptoms and treatments.
Approach: They propose a first-order logical reasoning model that uses First-Order Logic to model intricate relationships between diseases and treatments.
Outcome: The proposed model captures the interplay of symptoms, diseases, and treatments, enhancing context comprehension.
ReasoningLM: Enabling Structural Subgraph Reasoning in Pre-trained Language Models for Question Answering over Knowledge Graph (2023.emnlp-main)

Copied to clipboard

Challenge: Question Answering over Knowledge Graph (KGQA) aims to find answer entities for natural language questions based on knowledge graphs.
Approach: They propose a subgraph-aware self-attention mechanism to imitate the graph neural network (GNN) based module to perform multi-hop reasoning on KG.
Outcome: The proposed method surpasses state-of-the-art models by a large margin even with fewer updated parameters and less training data.
MedCoT: Medical Chain of Thought via Hierarchical Expert (2024.emnlp-main)

Copied to clipboard

Challenge: Existing methods for medical visual question answering lack robustness and reasoning paths for real-world medical diagnostics.
Approach: They propose a hierarchical expert verification reasoning chain method to enhance interpretability and accuracy in medical visual question answering.
Outcome: The proposed method outperforms existing methods on four standard Med-VQA datasets.
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.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations