Challenge: Medical education resources are dense for common diseases, but sparse for under-covered conditions, atypical presentations, and fine-grained concept distinctions.
Approach: They propose a task where a model reconstructs missing educational units from a partially specified curriculum graph.
Outcome: The proposed model predicts omitted concepts, restores missing instructional links, and completes automatically verifiable teaching content.

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RRNorm: A Novel Framework for Chinese Disease Diagnoses Normalization via LLM-Driven Terminology Component Recognition and Reconstruction (2024.findings-acl)

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Challenge: Clinical Terminology Normalization (CTN) aims at finding standard terms from a given termbase for mentions extracted from clinical texts.
Approach: They propose a method that leverages reasoning capability of large language models to recognize components of terms and automate decomposition.
Outcome: The proposed strategy achieves state-of-the-art on the experimental dataset.
Balancing Knowledge Breadth and Task Depth for Effective Domain Adaptation Fine-Tuning (2026.findings-acl)

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Challenge: a lack of knowledge breadth and task depth can hinder curriculum learning in domains such as medicine and finance.
Approach: They propose a two-dimensional curriculum learning framework that coordinates model training along two orthogonal axes: the knowledge dimension and the task dimension.
Outcome: The proposed framework improves accuracy on medical evaluations by 2.49% and on financial evaluations 1.2% compared with the second-best method.
MedCoach: Enhancing Medical Reasoning in LLMs via Knowledge Graph-Augmented Chain-of-Thought Distillation (2026.findings-acl)

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Challenge: Existing methods for training specialized reasoning models for the medical domain are limited due to the scarcity of high-quality, large-scale Chain-of-Thought (CoT) data.
Approach: They propose a framework that introduces a dedicated coach role to guide the student model through question decomposition.
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Filter-then-Generate: Large Language Models with Structure-Text Adapter for Knowledge Graph Completion (2025.coling-main)

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Challenge: Empirical evidence suggests that LLMs perform worse than conventional KGC approaches.
Approach: They propose a filter-then-generate paradigm and a multiple-choice question format to harness the capability of LLMs while mitigating the issue casused by hallucinations.
Outcome: The proposed method achieves substantial performance gain compared to existing state-of-the-art methods.
Augmenting Black-box LLMs with Medical Textbooks for Biomedical Question Answering (2024.findings-emnlp)

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Challenge: Large-scale language models (LLMs) like ChatGPT have demonstrated impressive abilities in generating responses based on human instructions. however, their use in the medical domain can be challenging due to their lack of specific, in-depth knowledge.
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Enhancing Large Language Model for Knowledge Graph Completion via Structure-Aware Alignment-Tuning (2025.emnlp-main)

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Challenge: Existing knowledge graph completion methods ignore inconsistent representation spaces between natural language and graph structures, leading to duplicate works and time-consuming processes.
Approach: They propose a framework that enhances LLMs for KGC via structure-aware alignment-tuning to align graph embeddings with the natural language space through multi-task contrastive learning.
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StrucSum: Graph-Structured Reasoning for Long Document Extractive Summarization with LLMs (2026.findings-eacl)

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Challenge: Large language models (LLMs) have shown strong performance in zero-shot summarization, but struggle to model document structure and identify salient information in long texts.
Approach: They propose a training-free prompting framework that injects structural signals into prompts via sentence-level graph structures.
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Exploring Graph Learning Tasks with Pure LLMs: A Comprehensive Benchmark and Investigation (2026.findings-acl)

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Challenge: Recent studies focus on performance benchmarks without fully comparing LLMs to graph learning models.
Approach: They evaluate off-the-shelf and instruction-tuned graph learning models across a variety of scenarios.
Outcome: The proposed models outperform traditional graph learning models in few-shot settings, the authors show . their models out perform models with instruction tuning, and they show excellent generalization and robustness.
Filling in the Mechanisms: How do LMs Learn Filler-Gap Dependencies under Developmental Constraints? (2026.findings-acl)

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Challenge: Language models lack language-specific biases, yet still posit some important syntactic generalizations.
Approach: They applied Distributed Alignment Search to checkpoints of a language model from the BabyLM challenge to evaluate whether representations of filler-gap dependencies transfer between wh-questions and topicalization.
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CGBridge: Bridging Code Graphs and Large Language Models for Better Structure-Aware Code Understanding (2026.findings-acl)

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Challenge: Existing structure-aware approaches treat structure as serialized text prompts or auxiliary training objectives, failing to provide explicit guidance during inference.
Approach: They propose a plug-and-play method that enhances Large Language Models with Code Graph information through an external, trainable Bridge module.
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