Overlap, Unique and Conflict: Can LLMs Extract What They Can Recognize?

arXiv:2609.38799 · cs.CL · Submitted 2026-09-30 · Read on arXiv

cs.CL

Submitted: 2026-09-30

Updated: 2026-09-30

Terminology

• Benchmark — 21× in this paper · explained in 2 episodes
• Alignment — 17× in this paper · explained in 1 episode
• GRPO — 17× in this paper · explained in 6 episodes
• F1 Score — 14× in this paper · explained in 3 episodes
• Q-learning — 13× in this paper · explained in 3 episodes
• Fine-Tuning — 8× in this paper · explained in 5 episodes
• Adaptation — 6× in this paper · explained in 1 episode
• Adversarial Training — 6× in this paper · explained in 1 episode
• Commentary — 6× in this paper · explained in 2 episodes
• Generalization — 6× in this paper · explained in 13 episodes
• Adversarial Examples — 5× in this paper · explained in 2 episodes
• Domain Adaptation — 5× in this paper · explained in 5 episodes
• Reinforcement Learning — 5× in this paper · explained in 4 episodes
• Core — 4× in this paper · explained in 2 episodes
• Verifiable Rewards — 4× in this paper · explained in 2 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Consistency — 3× in this paper · explained in 3 episodes
• Synthetic Data Generation — 3× in this paper · explained in 5 episodes
• Verification — 3× in this paper · explained in 2 episodes
• Actor — 2× in this paper · explained in 3 episodes
• Annotator Agreement — 2× in this paper · explained in 1 episode
• Benchmarking — 2× in this paper · explained in 3 episodes
• Parameter-Efficient Fine-Tuning — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Ablation Studies — 1× in this paper · explained in 1 episode
• Adversarial Attacks — 1× in this paper · explained in 1 episode
• Computational overhead — 1× in this paper · explained in 2 episodes
• Direct Preference Optimization (DPO) — 1× in this paper · explained in 9 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Domain Shift — 1× in this paper · explained in 4 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Group Relative Policy Optimization — 1× in this paper
• Group Relative Policy Optimization (GRPO) — 1× in this paper · explained in 5 episodes
• Natural Language Inference (NLI) — 1× in this paper · explained in 2 episodes
• Pairwise Comparisons — 1× in this paper · explained in 2 episodes
• Reasoning Traces — 1× in this paper · explained in 3 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Supervised Fine-Tuning (SFT) — 1× in this paper · explained in 8 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• Phi-4 Technical Report
• NVIDIA Nemotron 3: Efficient and Open Intelligence
• A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portal
• Reinforcement Learning with Verifiable Rewards: GRPO's Effective Loss, Dynamics, and Success Amplification
• Synthetic Data Generation Using Large Language Models: Advances in Text and Code
• Olmo 3
• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
• HybridFlow: A Flexible and Efficient RLHF Framework
• Lessons from Training Grounded LLMs with Verifiable Rewards
• Gemma 4 Technical Report
• Qwen3 Technical Report
• DRIVE: Data Curation Best Practices for Reinforcement Learning with Verifiable Reward in Competitive Code Generation

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