Teaching LLMs Temporal Critique for Ex-Ante Reasoning
A recent paper on arXiv (2605.14636) explores the shortcomings of large language models (LLMs) in ex-ante reasoning, which involves responding to questions based on prior knowledge without relying on future information. The researchers discovered that the way cutoff information is presented significantly affects temporal leakage; explicit cutoff phrases are more effective than implicit historical contexts, and prefix limitations are better at minimizing leakage compared to suffix ones. Although prompting can guide models into a specific temporal context, it does not allow them to confirm the appropriateness of that temporal frame. The study concludes that supervised fine-tuning alone is inadequate, as ex-ante accuracy is not an inherent characteristic of the answers provided.
Key facts
- arXiv paper 2605.14636 studies LLM failure in ex-ante reasoning.
- Temporal leakage occurs when models use knowledge from after the cutoff.
- Explicit cutoff statements reduce leakage better than implicit historical framings.
- Prefix constraints are more effective than suffix constraints.
- Prompting alone cannot verify temporal admissibility.
- Supervised fine-tuning is insufficient for ex-ante correctness.
- Ex-ante correctness is not intrinsic to an answer.
- The paper is available at https://arxiv.org/abs/2605.14636.
Entities
Institutions
- arXiv