Memorization vs. Reasoning: Updating LLM s with New Knowledge (2025)

Abstract: Large language models (LLMs) encode vast amounts of pre-trained knowledge in their parameters, but updating them as real-world information evolves remains a challenge. Existing methodologies and benchmarks primarily target entity substitutions, failing to capture the full breadth of complex real-world dynamics. In this paper, we introduce Knowledge Update Playground (KUP), an automatic pipeline for simulating realistic knowledge updates reflected in an evidence corpus. KUP’s evaluation framework includes direct and indirect probes to both test memorization of updated facts and reasoning over them, for any update learning methods. Next, we present a lightweight method called memory conditioned training (MCT), which conditions tokens in the update corpus on self-generated ”memory” tokens during training. Our strategy encourages LLMs to surface and reason over newly memorized knowledge at inference. Our results on two LLM families show that (1) KUP benchmark is highly challenging, with the best CPT models achieving <2% in indirect probing setting (reasoning) and (2) MCT training significantly outperforms prior continued pre-training (CPT) baselines, improving direct probing (memorization) results by up to 25.4%.

## introduction the parametric knowledge of large language models (llms) @xcite remains mostly static @xcite after the pre-training stage, whereas knowledge in the world continues to change. even within pre-training data, knowledge from recent years can conflict with earlier knowledge. but, the auto-regressive training objective biases llms toward surfacing more frequent but not necessarily recent knowledge @xcite . retrieval-augmented generation (rag) mitigates these issues to some extent cnn news as communicated on march 2, 2022, h&m group paused all sales in russia […] ## memory conditioned training (mct) next, we describe a lightweight learning method, "memory conditioning," and explain how to apply it during continued pre-training (cpt) and at inference time for knowledge update corpora. ## experiments we use llama-3.1-8b (llama; @xcite ) and mistral-7b-v0.3 (mistral; jiang et al. ( 2023 )) for all experiments. we train each model for 1 epoch on @xmath0. we follow the recipe in @xcite and include 1% replay data from redpajama @xcite for all models and baselines (described below). we use a learning rate of 1e-05 for all our experiments. we run both training and inference on two nvidia h100 gpus. baselines we compare our training method mct against the following baselines: (1) standard cpt that directly trains m t on the new cor-pus @xmath1. (2) cpt with rephrase, that augments the data in new corpus @xmath2.g., reddit posts, podcast transcripts) @xcite . we follow @xcite and use a strong llm (gpt-4o) to curate these data augmentations for each synthesized evidence news article in @xmath3. note that many update pairs (f old e , f new e ) in kup are not and cannot be as structured as entitysubstituted changes; prior knowledge editing methods @xcite are not straightforwardly applicable. moreover, recent work @xcite has shown that standard cpt outperforms these more specialized methods. ## analysis analysis of cot for recalling memory to understand what @xmath4, we manually annotate 100 such cot traces, like the example in we find that: copying mcq options verbatim (case i) accounts for 25% of the cots. when the cot contains "recalled" memory, we find that, on average, it includes 1.68 concrete details pulled from evidence news articles. 58% of these details are verifiably grounded in d @xmath6. at an example level, 35% of the examples do not have any hallucinated details. continued pre-training (cpt) perplexity does not suggest how well @xmath7, we ask, "does lower perplexity align with higher downstream performance on kup?" we report results in knowledge conflict datasets to study llm behaviors in presence of misinformation and outdated information, prior work @xcite @xcite ) construct simple, synthetic knowledge conflicts using entitysubstitutions. other lines of work localize and analyze model intra-memory conflicts and effective knowledge cutoffs @xcite . to assess model's ability to provide update-to-date information in the real world, prior benchmarks @xcite @xcite ) also try to collect knowledge updates and evaluate llms' behaviors in the real world. notably, @xcite show that rag frameworks fail for simple queries as real-world data is extremely noisy. ## conclusion in this paper, we introduce the knowledge update playground (kup), a novel framework designed to systemically study the effectiveness of different continued pre-training (cpt) methods for updating large language models' parameters with evolving knowledge. unlike prior benchmarks that rely on entity-substitution frameworks, kup curates synthetic datasets that can capture the nuances and complexity of real-world knowledge dynamics. to evaluate llms' memorization and reasoning capabilities over knowledge updates, we also develop an evaluation toolkit, kupeval, which includes both direct and indirect probing tests. additionally, we propose memory conditioned training (mct), a lightweight yet effective continued pre-training technique. mct outperforms cpt baselines multiple direct probing tests (both mcq and free-form qa). nevertheless, indirect probing tasks remain particularly challenging for all existing training methods, and we encourage future research to continue to work on this problem.