KA2L: A Knowledge-Aware Active Learning Framework for LLMs

• URL: http://arxiv.org/abs/2603.17566v1
• Date: Wed, 18 Mar 2026 10:16:07 GMT
• Title: KA2L: A Knowledge-Aware Active Learning Framework for LLMs
• Abstract summary: This study investigates the depth of domain-specific knowledge comprehension by large language models (LLMs)<n>We introduce the Knowledge-Aware Active Learning framework to aid in constructing unknowable questions through latent space analysis.<n>Results indicate that KA2L not only significantly reduces 50% annotation and costs across two open-domain and one vertical-domain dataset.
• Score: 11.702131167960923
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fine-tuning large language models (LLMs) with high-quality knowledge has been shown to enhance their performance effectively. However, there is a paucity of research on the depth of domain-specific knowledge comprehension by LLMs and the application of targeted active learning to improve their expertise. To address this gap, we introduce the Knowledge-Aware Active Learning (KA2L) framework. This framework assesses LLMs' mastery of specific knowledge points to aid in constructing unanswerable or unknowable questions through latent space analysis. This active learning strategy enhances training efficiency by focusing on knowledge the model has yet to master, thereby minimizing redundancy in learning already acquired information. This study innovatively employs a knowledge distribution probing technique to examine the hidden states of specific Transformer layers and identify the distribution of known and unknown knowledge within the LLM. Additionally, a hidden-state decoding method is proposed to generate numerous unknown questions in natural language from the latent knowledge space. In our experiments, we selected nine open-source LLMs to validate the effectiveness of the proposed framework. Results indicate that KA2L not only significantly reduces 50% annotation and computation costs across two open-domain and one vertical-domain dataset but also achieves better performance, offering valuable insights into active learning strategies for LLMs. The code is available at https://anonymous.4open.science/r/KA2L-F15C.

Related papers

• KUDA: Knowledge Unlearning by Deviating Representation for Large Language Models [26.418820118903852]
  Large language models (LLMs) acquire a large amount of knowledge through pre-training on vast and diverse corpora.<n>LLMs unlearning is a promising technique to reduce risks associated with sensitive, copyrighted, or harmful content in training data.<n>We propose Knowledge Unlearning by Deviating representAtion (KUDA) to achieve effective unlearning at the knowledge level of LLMs.
  arXiv  Detail & Related papers  (2026-02-22T17:16:49Z)
• Unveiling Knowledge Utilization Mechanisms in LLM-based Retrieval-Augmented Generation [77.10390725623125]
  retrieval-augmented generation (RAG) is widely employed to expand their knowledge scope.<n>Since RAG has shown promise in knowledge-intensive tasks like open-domain question answering, its broader application to complex tasks and intelligent assistants has further advanced its utility.<n>We present a systematic investigation of the intrinsic mechanisms by which RAGs integrate internal (parametric) and external (retrieved) knowledge.
  arXiv  Detail & Related papers  (2025-05-17T13:13:13Z)
• Enhancing LLM Knowledge Learning through Generalization [73.16975077770765]
  We show that an LLM's ability to continually predict the same factual knowledge tokens given diverse paraphrased contexts is positively correlated with its capacity to extract that knowledge via question-answering.<n>We propose two strategies to enhance LLMs' ability to predict the same knowledge tokens given varied contexts, thereby enhancing knowledge acquisition.
  arXiv  Detail & Related papers  (2025-03-05T17:56:20Z)
• KaLM: Knowledge-aligned Autoregressive Language Modeling via Dual-view Knowledge Graph Contrastive Learning [74.21524111840652]
  This paper proposes textbfKaLM, a textitKnowledge-aligned Language Modeling approach.<n>It fine-tunes autoregressive large language models to align with KG knowledge via the joint objective of explicit knowledge alignment and implicit knowledge alignment.<n> Notably, our method achieves a significant performance boost in evaluations of knowledge-driven tasks.
  arXiv  Detail & Related papers  (2024-12-06T11:08:24Z)
• FAC$^2$E: Better Understanding Large Language Model Capabilities by Dissociating Language and Cognition [56.76951887823882]
  Large language models (LLMs) are primarily evaluated by overall performance on various text understanding and generation tasks. We present FAC$2$E, a framework for Fine-grAined and Cognition-grounded LLMs' Capability Evaluation.
  arXiv  Detail & Related papers  (2024-02-29T21:05:37Z)
• Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMs [60.40396361115776]
  This paper introduces a novel collaborative approach, namely SlimPLM, that detects missing knowledge in large language models (LLMs) with a slim proxy model. We employ a proxy model which has far fewer parameters, and take its answers as answers. Heuristic answers are then utilized to predict the knowledge required to answer the user question, as well as the known and unknown knowledge within the LLM.
  arXiv  Detail & Related papers  (2024-02-19T11:11:08Z)
• InfuserKI: Enhancing Large Language Models with Knowledge Graphs via Infuser-Guided Knowledge Integration [58.61492157691623]
  Methods for integrating knowledge have been developed, which augment LLMs with domain-specific knowledge graphs through external modules.<n>Our research focuses on a novel problem: efficiently integrating unknown knowledge into LLMs without unnecessary overlap of known knowledge.<n>A risk of introducing new knowledge is the potential forgetting of existing knowledge.
  arXiv  Detail & Related papers  (2024-02-18T03:36:26Z)
• Don't Hallucinate, Abstain: Identifying LLM Knowledge Gaps via Multi-LLM Collaboration [39.603649838876294]
  We study approaches to identify LLM knowledge gaps and abstain from answering questions when knowledge gaps are present. Motivated by their failures in self-reflection and over-reliance on held-out sets, we propose two novel approaches.
  arXiv  Detail & Related papers  (2024-02-01T06:11:49Z)
• Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation [109.8527403904657]
  We show that large language models (LLMs) possess unwavering confidence in their knowledge and cannot handle the conflict between internal and external knowledge well. Retrieval augmentation proves to be an effective approach in enhancing LLMs' awareness of knowledge boundaries. We propose a simple method to dynamically utilize supporting documents with our judgement strategy.
  arXiv  Detail & Related papers  (2023-07-20T16:46:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.