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The Future of Facts: Tracing the Factual Generation-Verification Gap

NeurIPS 2026 · Large language models › Retrieval and knowledge · approach: Knowledge editing in LLMs · 0 citations (OpenAlex)

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Controlled fine-tuning study on synthetic facts across four open model families shows verification is learned before generation, survives continual learning better, and factual updates leave models in a multi-verse state, with natural-experiment checks on frontier models.

Problem

How training mechanisms (acquisition, continual learning, updating) shape the generation-verification gap on factual knowledge, which remains poorly understood because training data is opaque and confounded.

Approach

Uses synthetic facts with paraphrased training sentences and controlled fine-tuning to trace generation and verification separately across three life-cycle phases, and exploits real-world coverage variation (S&P 500, NBA, lottery, Billboard) to test frontier models.

Key results

• Verification is learned before generation during acquisition across all four families and scales; gap closes with sufficient exposure but is invisible in training loss.
• Verification is more robust than generation under continual learning on T-REx, with generation collapsing sharply while verification settles at a higher floor.
• After updating, generation flips to the new answer while verification still accepts the old answer, a multi-verse state; on GPT 5.4 low reasoning, NBA 2013 example gives U_G=0.50 and U_V(✓)=0.80 vs U_V(×)=0.88.

Surprising findings

• Verification accepts both old and new answers after updating, even though generation has fully shifted.
• Increased reasoning effort does not improve generation or overall verification utility, and widens affirmation bias for OpenAI models.

Limitations

• Single-hop synthetic facts with clean paraphrases; likely not representative of messy natural training data.
• Facts injected during post-training (SFT) rather than pretraining.
• Frontier natural experiments are confounded by possible evaluation-context recognition and lack of training-data access.

Experimental setup

Datasets: T-REx S&P 500 closing prices NBA game scores Mega Millions lottery numbers Billboard Hot 100

Benchmarks: synthetic fact acquisition/continual learning/update tests natural-experiment coverage datasets (S&P 500, NBA, lottery, Billboard)

Metrics: generation accuracy balanced verification accuracy utility U_V' validation loss GV-gap

Baselines: GPT 5.4 Gemini 3 Flash

Builds on: Best-of-N rejection sampling T-REx DPO

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