Unverified50% confidenceFactExact time
JaNLI corresponds to the natural language inference task, judging whether a premise sentence and a hypothesis sentence are entailment, contradiction, or neutral, and often includes trap sentences to test semantic understanding.
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Long-term
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10/14/2026
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UnverifiedThe core logic of Membership Inference Attacks (MIA) is: when a language model assigns an extremely low perplexity to a sentence, it is inferred that the sentence was in the training data.66% similarUnverifiedExisting evaluation benchmarks tend to fix scenarios and phrasing, allowing models to pass tests by memorizing standard answers while failing in real interactions where phrasing varies—a phenomenon known as benchmark overfitting.64% similarUnverifiedLanguage models have an inherent tendency to hallucinate, potentially generating proof steps that appear plausible but contain logical flaws; therefore, combining AI output with formal verification tools is the key path to ensuring mathematical correctness64% similarUnverifiedConstructing isomorphic variants of test sets (same logical structure but with surface symbols replaced) can force models to rely on genuine logical reasoning rather than memorization to answer.63% similarUnverifiedThe proof agent employs an architecture in which a large language model interacts with a formal verifier: the LLM generates sequences of proof strategies, the Lean compiler verifies each reasoning step in real time, and upon failure adjusts based on error messages, forming a closed loop of generation–verification–correction.63% similar
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