BeLink

• Paper:
• PDF: [[|论文.pdf]]
• Venue / year:
• Topic: Generative LLM rerank

1. To Understand List

2. Abstract

• Model / method: A BEL pipeline using re-ranking stage through generative models
• Problem: Biomedical Entity Linking (BEL) with large language models (LLMs) remains computationally inefficient and challenging to deploy in practical settings.
• Method: Propose a set-wise instruction-tuning formulation
• Result: Our method demonstrates strong performance on multiple BEL benchmarks, yielding significant improvements in linking accuracy (3%–24%) while reducing inference time compared to the state-of-the-art.

• Keywords:

3. Problem-Solution Chain

Introduction Notes

• Core problem:
• Why it matters:
• Previous methods:
• Limitations:
• Main contributions:

4. Related Works

What this paper does

Prior work map

5. Key Figure / Pipeline

• Figure:
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• Output:
• Main modules:
• Difference from prior methods:
• My explanation in plain language:

Questions about the figure

6. Methods

Core idea

Step-by-step process

1. Raw input:
2. Operation:
3. Model / algorithm:
4. Intermediate representation:
5. Training objective:
6. Final output:

Formula / algorithm notes

7. Experiments

Setup

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• Baselines:
• Metrics:
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Main results

Ablation / analysis

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8. Conclusion

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• Next action: