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Pre-training Limited Memory Language Models with Internal and External Knowledge

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.5Download Report PDF
Pretrained Large Language ModelsKnowledge Offloading
Abstract:

Neural language models are black-boxes--both linguistic patterns and factual knowledge are distributed across billions of opaque parameters. This entangled encoding makes it difficult to reliably inspect, verify, or update specific facts. We introduce Limited Memory Language Models (LMLM), a new class of language models that externalizes factual knowledge to external database during pre-training rather than memorizing them. Our pre-training approach strategically masks externally retrieved factual values from the training loss, thereby teaching the model to perform targeted lookups rather than relying on memorization in model weights. Our experiments demonstrate that LMLMs achieve competitive performance compared to significantly larger LLMs on standard benchmarks, while offering the advantages of explicit, editable, and verifiable knowledge bases.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Taxonomy

Core-task Taxonomy Papers
25
3
Claimed Contributions
0
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: externalizing factual knowledge from language model parameters during pre-training. The field addresses a fundamental tension in modern language models: whether to store factual knowledge implicitly within billions of parameters or to externalize it into retrievable memory structures. The taxonomy reflects five main branches that capture different facets of this challenge. External Knowledge Integration Architectures explores how retrieval mechanisms can be woven into model designs, with early work like REALM[1] pioneering retrieval-augmented pre-training and more recent efforts such as Retrieval-Native Models[13] pushing toward architectures that treat external memory as a first-class component. Knowledge-Aware Pre-Training Strategies examines training objectives and curricula that encourage models to rely on external stores, while Knowledge Storage and Extraction Analysis investigates where and how factual information is encoded—whether in parameters or in explicit memory modules. Post-Training Knowledge Manipulation considers techniques for editing or updating facts after initial training, and Domain-Specific Knowledge Externalization Applications demonstrates these ideas in specialized contexts like biomedicine or food science. A particularly active line of work contrasts pure retrieval-augmented approaches with hybrid memory designs. Some studies focus on scaling external memory capacity during pre-training (e.g., Large Memory Pretraining[8]), while others like Memory3[5] and Explicit Memory Modeling[9] explore how to balance parametric and non-parametric storage. Limited Memory Pretraining[0] sits squarely within the Retrieval-Augmented Pre-Training cluster, emphasizing constrained parametric capacity to force reliance on external knowledge sources. This contrasts with neighbors like REALM[1], which introduced retrieval-augmented pre-training but did not explicitly limit model size, and Retrieval-Native Models[13], which advocates for architectures designed from the ground up to depend on retrieval rather than retrofitting it onto standard transformers. The central trade-off across these branches remains whether externalizing knowledge improves factual accuracy and updatability enough to justify the added complexity of retrieval infrastructure.

Claimed Contributions

Limited Memory Language Models (LMLM)

LMLM is a novel class of language models designed to offload entity-level factual knowledge to an external database during pre-training instead of storing it in model parameters. This approach decouples factual memorization from language understanding, enabling more efficient use of model capacity and providing explicit, editable, and verifiable knowledge bases.

0 retrieved papers
Pre-training approach with lookup masking

The authors propose a modified pre-training procedure that excludes retrieved factual values from the loss computation. This design discourages the model from memorizing facts and instead teaches it to generate database lookup calls, systematically separating factual knowledge from neural weights during training.

0 retrieved papers
Integrated solution for data preparation, pre-training, and inference

The authors develop a complete framework that includes automated knowledge extraction using a distilled annotator model to prepare training data, a modified pre-training loss that masks retrieved values, and an inference procedure where the model interleaves text generation with database lookups to ground outputs on retrieved facts.

0 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Limited Memory Language Models (LMLM)

LMLM is a novel class of language models designed to offload entity-level factual knowledge to an external database during pre-training instead of storing it in model parameters. This approach decouples factual memorization from language understanding, enabling more efficient use of model capacity and providing explicit, editable, and verifiable knowledge bases.

Contribution

Pre-training approach with lookup masking

The authors propose a modified pre-training procedure that excludes retrieved factual values from the loss computation. This design discourages the model from memorizing facts and instead teaches it to generate database lookup calls, systematically separating factual knowledge from neural weights during training.

Contribution

Integrated solution for data preparation, pre-training, and inference

The authors develop a complete framework that includes automated knowledge extraction using a distilled annotator model to prepare training data, a modified pre-training loss that masks retrieved values, and an inference procedure where the model interleaves text generation with database lookups to ground outputs on retrieved facts.

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