Papers with expertise-intensive process
EC-RAFT: Automated Generation of Clinical Trial Eligibility Criteria through Retrieval-Augmented Fine-Tuning (2025.findings-acl)
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| Challenge: | Eligibility criteria (EC) are critical components of clinical trial design, specifying parameters for participant inclusion and exclusion. |
| Approach: | They propose a method that utilizes Retrieval-Augmented Fine-Tuning to generate structured and cohesive EC directly from clinical trial titles and descriptions. |
| Outcome: | The proposed method outperforms Llama-3.1-8B-Instruct and Llm-as-a-Judge models in BERTScore and EC score. |
Can LLMs Identify Critical Limitations within Scientific Research? A Systematic Evaluation on AI Research Papers (2025.acl-long)
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| Challenge: | Recent advances in large language models (LLMs) have demonstrated remarkable capabilities across a variety of scientific tasks, such as answering questions about scientific papers, writing scientific papers and retrieving related works. |
| Approach: | They propose a taxonomy of limitation types in scientific research with a focus on AI to evaluate their ability to support early-stage feedback and complement human peer review. |
| Outcome: | The proposed model enhances the ability of LLM systems to generate limitations in research papers, enabling them to provide more concrete and constructive feedback. |