Papers with ReCo

2 papers
Rewriting the Code: A Simple Method for Large Language Model Augmented Code Search (2024.acl-long)

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Challenge: Experimental results show that ReCo significantly boosts retrieval accuracy across sparse, zero-shot dense and fine-tuned dense search settings.
Approach: They propose a generation-augmented retrieval framework that additionally Rewrites the Code (ReCo) within the codebase for style normalization.
Outcome: The proposed method significantly boosts retrieval accuracy across sparse, zero-shot dense, and fine-tuned dense retrieval settings in diverse search scenarios.
ReCo: Reliable Causal Chain Reasoning via Structural Causal Recurrent Neural Networks (2022.emnlp-main)

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Challenge: Causal chain reasoning models suffer from two main transitive problems: threshold effect and scene drift.
Approach: They propose a framework that uses exogenous variables to represent causal pairs and estimates the threshold and scene contradictions using structural causal recurrent neural networks.
Outcome: The proposed framework outperforms baselines on Chinese and English CCR datasets.

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