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Diagnosing and Remedying Knowledge Deficiencies in LLMs via Label-free Curricular Meaningful Learning

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Deficiency DiagnosisData SynthesisLLMs Reasoning
Abstract:

Large Language Models (LLMs) have demonstrated impressive generalization ability by learning from extensive unlabeled text. However, they still exhibit reasoning mistakes, which can affect their trustworthiness and reliability. Although users can interact with LLMs and provide diverse and comprehensive queries to expose the flaws of LLMs, obtaining sufficient and effective feedback is demanding. Furthermore, comprehensively evaluating LLMs with limited labeled samples is difficult. These make it a challenge to diagnose and remedy the deficiencies in LLMs through rich label-free user queries. To tackle this challenge and considersing that LLMs' reasoning mistakes often stem from knowledge deficiencies, we propose label-free curricular meaningful learning (LaMer), which first employs relative entropy to diagnose and quantify knowledge deficiencies of LLMs in a label-free setting. Then, LaMer adaptively synthesizes augmentation data based on deficiency severity and progressively remedies them with a curricular remedy strategy. Experiments show that LaMer effectively diagnoses and remedies knowledge deficiencies in LLMs, improving various LLMs across seven out-of-distribution (OOD) reasoning benchmarks, achieving comparable results to baselines with only 40% training data. LaMer even surpasses methods that rely on labeled data for deficiency diagnosis. In application, LaMer offers a diagnostic tool for efficient LLM development.

Disclaimer
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.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes LaMer, a framework for diagnosing and remedying knowledge deficiencies in LLMs through label-free curricular learning and adaptive data synthesis. It resides in the 'Curriculum Learning and Adaptive Data Synthesis' leaf under 'Training-Based Factuality Improvement', which contains only two papers total. This is a relatively sparse research direction within the broader taxonomy of fifty papers across thirty-six topics, suggesting the specific combination of curriculum-based training and label-free deficiency diagnosis is not yet heavily explored in the literature.

The taxonomy reveals that most factuality work concentrates on hallucination detection, self-correction, retrieval-augmented generation, and knowledge editing—branches with three to nine papers each. LaMer's training-based approach contrasts with inference-time methods like Chain of Verification or CRITIC, and differs from parameter-editing techniques surveyed in Knowledge Editing frameworks. The sibling paper in the same leaf, Structural Entropy Agent, shares the adaptive synthesis theme but appears to employ different algorithmic mechanisms. Neighboring leaves in 'Fine-Tuning for Factuality and Reasoning' address similar training objectives but lack the curriculum and label-free diagnosis emphasis.

Across three contributions, the analysis examined twenty-one candidate papers with no clear refutations found. The label-free diagnosis contribution reviewed eight candidates with zero refutable matches, the curricular remediation strategy examined ten candidates with none refuting, and the overall LaMer framework checked three candidates with no overlaps. These statistics reflect a limited semantic search scope rather than exhaustive coverage, indicating that among the top-ranked candidates retrieved, none provided directly overlapping prior work on the specific combination of label-free entropy-based diagnosis and curricular data synthesis.

Given the sparse leaf occupancy and absence of refutations among examined candidates, the work appears to occupy a relatively underexplored niche within training-based factuality improvement. However, the analysis is constrained by the twenty-one-paper search scope and does not capture the full breadth of curriculum learning or data augmentation literature outside this taxonomy. The novelty assessment is therefore provisional, contingent on the limited retrieval context provided.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
21
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: diagnosing and remedying knowledge deficiencies in large language models. The field has evolved into a rich landscape organized around several complementary branches. Hallucination Detection and Characterization focuses on identifying when models produce unfaithful outputs, with works like Sirens Song Survey[3] and Metamorphic Testing Hallucinations[5] exploring detection mechanisms. Factuality Enhancement via Self-Correction and Verification examines methods such as Chain of Verification[1] and CRITIC[25] that enable models to refine their own outputs. Knowledge Integration and Retrieval-Augmented Generation addresses how external knowledge sources can supplement parametric memory, while Knowledge Editing and Model Updating (e.g., Knowledge Editing Survey[17], CKnowEdit[40]) targets surgical modifications to correct specific facts. Training-Based Factuality Improvement explores learning paradigms that instill more accurate knowledge during model development, and Knowledge Boundary Analysis (e.g., Knowledge Boundary Survey[41]) investigates what models know versus what they hallucinate. Domain-Specific Knowledge Assessment branches into specialized areas like Clinical Knowledge Encoding[14] and ChatGPT Bioinformatics[48], while other branches examine factuality in reasoning tasks and evolving information. Within Training-Based Factuality Improvement, a central tension emerges between curriculum design, data quality, and adaptive synthesis strategies. Some approaches emphasize structured learning schedules or meaningful ordering of training examples, while others focus on generating high-fidelity synthetic data or filtering noisy sources. Curricular Meaningful Learning[0] sits squarely in this training-oriented branch, specifically within Curriculum Learning and Adaptive Data Synthesis, where it addresses how to sequence or synthesize training material to reduce knowledge gaps. This contrasts with post-hoc correction methods like Chain of Verification[1] or external retrieval strategies, instead aiming to bake factuality into the learning process itself. Nearby, Structural Entropy Agent[24] explores related adaptive mechanisms, though from a different algorithmic angle. The broader question remains how curriculum and data synthesis compare to editing-based or retrieval-based remedies in terms of scalability and long-term knowledge retention.

Claimed Contributions

Label-free knowledge deficiency diagnosis using relative entropy

The authors propose a method that uses relative entropy to automatically identify and quantify knowledge deficiencies in large language models without requiring labeled data. This approach compares predictive distributions before and after introducing knowledge to estimate what the model lacks or cannot properly apply.

8 retrieved papers
Curricular meaningful learning for deficiency remediation

The authors design a two-part strategy that first adaptively synthesizes varying numbers of training examples based on deficiency severity (meaningful learning), then progressively trains the model from minor to severe deficiencies (curricular remedy). This approach is inspired by how humans learn new knowledge across diverse situations.

10 retrieved papers
LaMer framework for diagnosing and remedying LLM deficiencies

The authors introduce LaMer, an end-to-end framework that combines knowledge extraction, label-free deficiency diagnosis via relative entropy, and curricular meaningful learning to improve LLMs. The framework enables efficient LLM enhancement using unlabeled user queries and offers a diagnostic tool for LLM development.

3 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Label-free knowledge deficiency diagnosis using relative entropy

The authors propose a method that uses relative entropy to automatically identify and quantify knowledge deficiencies in large language models without requiring labeled data. This approach compares predictive distributions before and after introducing knowledge to estimate what the model lacks or cannot properly apply.

Contribution

Curricular meaningful learning for deficiency remediation

The authors design a two-part strategy that first adaptively synthesizes varying numbers of training examples based on deficiency severity (meaningful learning), then progressively trains the model from minor to severe deficiencies (curricular remedy). This approach is inspired by how humans learn new knowledge across diverse situations.

Contribution

LaMer framework for diagnosing and remedying LLM deficiencies

The authors introduce LaMer, an end-to-end framework that combines knowledge extraction, label-free deficiency diagnosis via relative entropy, and curricular meaningful learning to improve LLMs. The framework enables efficient LLM enhancement using unlabeled user queries and offers a diagnostic tool for LLM development.

Table of Contents

Diagnosing and Remedying Knowledge Deficiencies in LLMs via Label-free Curricular Meaningful Learning | Novelty Validation