Teaching Large Language Models When Not to Know: Learning Temporal Critique for Ex-Ante Reasoning

TL;DR

This paper investigates how large language models fail to reason correctly with temporal cutoffs and introduces a fine-tuning method, TCFT, to improve their ability to verify temporal admissibility of responses.

Contribution

The paper identifies limitations of prompting and supervised fine-tuning for temporal reasoning and proposes TCFT, a fine-tuning framework that enhances models' temporal verification capabilities.

Findings

TCFT reduces temporal leakage by over 40 percentage points.

Explicit cutoff prompts outperform implicit historical framings.

Fine-tuning with TCFT improves temporal admissibility judgment accuracy.

Abstract

Large language models (LLMs) often fail to reason under temporal cutoffs: when prompted to answer from the standpoint of an earlier time, they exploit knowledge that became available only later. We study this failure through the lens of ex-ante reasoning, where a model must rely exclusively on information knowable before a cutoff. Through a systematic analysis of prompt-level interventions, we find that temporal leakage is highly sensitive to cutoff formulation and instruction placement: explicit cutoff statements outperform implicit historical framings, and prefix constraints reduce leakage more effectively than suffix constraints. These findings indicate that prompting can steer models into a temporal frame, but does not endow them with the ability to verify whether a response is temporally admissible. We further argue that supervised fine-tuning is insufficient, since ex-ante correctness is not an intrinsic property of an answer, but a relation between the answer and the cutoff. To address this gap, we propose TCFT, a Temporal Critique Fine-Tuning framework that trains models to acquire cutoff-aware temporal verification. Given a query, a cutoff, and a candidate response, TCFT teaches the model to identify post-cutoff leakage, explain temporal boundary violations, and judge temporal admissibility. Experiments with Qwen2.5-7B-Instruct and Qwen2.5-14B-Instruct show that TCFT consistently outperforms prompting and SFT baselines, reducing average leakage by 41.89 and 37.79 percentage points, respectively.