CoVer: Conflict-Aware Claim Verification

arXiv:2609.00508 · cs.AI · Submitted 2026-09-01 · Read on arXiv

cs.AI

Submitted: 2026-09-01

Updated: 2026-09-01

Terminology

• Verification — 35× in this paper · explained in 2 episodes
• Accuracy — 29× in this paper · explained in 1 episode
• Aggregation — 18× in this paper · explained in 1 episode
• Benchmark — 11× in this paper · explained in 2 episodes
• Ablation — 10× in this paper · explained in 1 episode
• RAG — 10× in this paper · explained in 3 episodes
• Generative AI — 6× in this paper · explained in 2 episodes
• F1 Score — 5× in this paper · explained in 3 episodes
• Ablation study — 4× in this paper · explained in 1 episode
• Verifier — 4× in this paper · explained in 2 episodes
• Alignment — 3× in this paper · explained in 1 episode
• Provenance — 3× in this paper · explained in 3 episodes
• Generalizability — 2× in this paper · explained in 6 episodes
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Decision-Support Tool — 1× in this paper · explained in 2 episodes
• Elias — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Longitudinal Data — 1× in this paper · explained in 2 episodes
• Low-Resource Languages — 1× in this paper · explained in 4 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Selection Bias — 1× in this paper · explained in 4 episodes
• Structured Reasoning — 1× in this paper · explained in 3 episodes

Sources

• Resolving Conflicting Evidence in Automated Fact-Checking: A Study on Retrieval-Augmented LLMs
• Question Answering under Temporal Conflict: Evaluating and Organizing Evolving Knowledge with LLMs
• Retrieval-Augmented Generation with Conflicting Evidence
• Customized large language models can outperform Community Notes in correcting misinformation

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