Papers with CoV
Retrieving, Rethinking and Revising: The Chain-of-Verification Can Improve Retrieval Augmented Generation (2024.findings-emnlp)
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| Challenge: | Recent Retrieval Augmented Generation (RAG) aims to enhance Large Language Models . however, such approach can generate inconsistent answer with external references . |
| Approach: | They propose to integrate the verification module into the RAG to improve external retrieval correctness and internal generation consistency. |
| Outcome: | The proposed model can significantly surpass the state-of-the-art baselines using different LLM backbones. |
Can You Really Trust Code Copilot? Evaluating Large Language Models from a Code Security Perspective (2025.acl-long)
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| Challenge: | Existing code security benchmarks focus on one task and paradigm, such as code completion and generation, without comprehensive assessment across dimensions like secure code generation, vulnerability repair and discrimination. |
| Approach: | They propose a multi-task benchmark for comprehensive evaluation of LLM code security . they also propose VC-Judge, an improved judgment model that aligns closely with human experts . |
| Outcome: | The proposed model can evaluate LLM-generated programs for vulnerabilities in a more efficient and reliable way. |
CoV: Chain-of-View Prompting for Spatial Reasoning (2026.findings-acl)
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Haoyu Zhao, Akide Liu, Zeyu Zhang, Weijie Wang, Feng Chen, Ruihan Zhu, Gholamreza Haffari, Bohan Zhuang
| Challenge: | Embodied question answering requires collecting context that is distributed across multiple viewpoints . most recent vision–language models (VLMs) are constrained to a fixed and finite set of input views . |
| Approach: | They propose a training-free, test-time reasoning framework that transforms a VLM into an active viewpoint reasoner through a coarse-to-fine exploration process. |
| Outcome: | The proposed framework improves LLM-Match performance by 11.98% on four mainstream VLMs. |