KBAlign: Efficient Self Adaptation on Specific Knowledge Bases

• URL: http://arxiv.org/abs/2411.14790v4
• Date: Thu, 15 May 2025 13:02:21 GMT
• Title: KBAlign: Efficient Self Adaptation on Specific Knowledge Bases
• Authors: Zheni Zeng, Yuxuan Chen, Shi Yu, Ruobing Wang, Yukun Yan, Zhenghao Liu, Shuo Wang, Xu Han, Zhiyuan Liu, Maosong Sun,
• Abstract summary: We present KBAlign, a self-supervised framework that enhances RAG systems through efficient model adaptation.<n>Our key insight is to leverage the model's intrinsic capabilities for knowledge alignment through two innovative mechanisms.<n> Experiments demonstrate that KBAlign can achieve 90% of the performance gain obtained through GPT-4-supervised adaptation.
• Score: 73.34893326181046
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Although retrieval-augmented generation (RAG) remains essential for knowledge-based question answering (KBQA), current paradigms face critical challenges under specific domains. Existing methods struggle with targeted adaptation on small-scale KBs: vanilla unsupervised training exhibits poor effectiveness, while fine-tuning incurs prohibitive costs of external signals. We present KBAlign, a self-supervised framework that enhances RAG systems through efficient model adaptation. Our key insight is to leverage the model's intrinsic capabilities for knowledge alignment through two innovative mechanisms: multi-grained self-annotation that captures global knowledge for data construction, and iterative tuning that accelerates convergence through self verification. This framework enables cost-effective model adaptation to specific textual KBs, without human supervision or external model assistance. Experiments demonstrate that KBAlign can achieve 90\% of the performance gain obtained through GPT-4-supervised adaptation, while relying entirely on self-annotation of much smaller models. KBAlign significantly improves downstream QA accuracy across multiple domains with tiny costs, particularly benefiting scenarios requiring deep knowledge integration from specialized corpora. We release our experimental data, models, and process analyses to the community for further exploration (https://github.com/thunlp/KBAlign).

Related papers

• Forgetting: A New Mechanism Towards Better Large Language Model Fine-tuning [53.398270878295754]
  Supervised fine-tuning (SFT) plays a critical role for pretrained large language models (LLMs)<n>We suggest categorizing tokens within each corpus into two parts -- positive and negative tokens -- based on whether they are useful to improve model performance.<n>We conduct experiments on well-established benchmarks, finding that this forgetting mechanism not only improves overall model performance and also facilitate more diverse model responses.
  arXiv  Detail & Related papers  (2025-08-06T11:22:23Z)
• Augmented Reinforcement Learning Framework For Enhancing Decision-Making In Machine Learning Models Using External Agents [0.0]
  This work proposes a novel technique Augmented Reinforcement Learning framework for the improvement of decision-making capabilities.<n>The external agent can be anyone, like humans or automated scripts, that helps in decision path correction.<n>The framework incorporates two external agents that aid in course correction and the guarantee of quality data at all points of the training cycle.
  arXiv  Detail & Related papers  (2025-08-03T06:17:44Z)
• Active Learning Methods for Efficient Data Utilization and Model Performance Enhancement [5.4044723481768235]
  This paper gives a detailed overview of Active Learning (AL), which is a strategy in machine learning that helps models achieve better performance using fewer labeled examples. It introduces the basic concepts of AL and discusses how it is used in various fields such as computer vision, natural language processing, transfer learning, and real-world applications.
  arXiv  Detail & Related papers  (2025-04-21T20:42:13Z)
• UIPE: Enhancing LLM Unlearning by Removing Knowledge Related to Forgetting Targets [41.0340052199534]
  Large Language Models (LLMs) inevitably acquire harmful information during training on massive datasets. Existing unlearning methods focus on forgetting target data while overlooking the crucial impact of logically related knowledge on the effectiveness of unlearning. We propose Unlearning Improvement via Extrapolation (UIPE), a method that removes knowledge highly correlated with the forgetting targets.
  arXiv  Detail & Related papers  (2025-03-06T18:40:00Z)
• Effective LLM Knowledge Learning via Model Generalization [73.16975077770765]
  Large language models (LLMs) are trained on enormous documents that contain extensive world knowledge. It is still not well-understood how knowledge is acquired via autoregressive pre-training. In this paper, we focus on understanding and improving LLM knowledge learning.
  arXiv  Detail & Related papers  (2025-03-05T17:56:20Z)
• LEKA:LLM-Enhanced Knowledge Augmentation [24.552995956148145]
  Humans excel in analogical learning and knowledge transfer. Models would transition from passively acquiring to actively accessing and learning from knowledge. We develop a knowledge augmentation method LEKA for knowledge transfer.
  arXiv  Detail & Related papers  (2025-01-29T17:44:57Z)
• Self-Improvement in Language Models: The Sharpening Mechanism [70.9248553790022]
  We offer a new perspective on the capabilities of self-improvement through a lens we refer to as sharpening.<n>Motivated by the observation that language models are often better at verifying response quality than they are at generating correct responses, we formalize self-improvement as using the model itself as a verifier during post-training.<n>We analyze two natural families of self-improvement algorithms based on SFT and RLHF.
  arXiv  Detail & Related papers  (2024-12-02T20:24:17Z)
• Leveraging Open Knowledge for Advancing Task Expertise in Large Language Models [36.172093066234794]
  We introduce few human-annotated samples (i.e., K-shot) for advancing task expertise of large language models with open knowledge. A mixture-of-expert (MoE) system is built to make the best use of individual-yet-complementary knowledge between multiple experts.
  arXiv  Detail & Related papers  (2024-08-28T16:28:07Z)
• Not All Contexts Are Equal: Teaching LLMs Credibility-aware Generation [47.42366169887162]
  Credibility-aware Generation (CAG) aims to equip models with the ability to discern and process information based on its credibility. Our model can effectively understand and utilize credibility for generation, significantly outperform other models with retrieval augmentation, and exhibit resilience against the disruption caused by noisy documents.
  arXiv  Detail & Related papers  (2024-04-10T07:56:26Z)
• TRELM: Towards Robust and Efficient Pre-training for Knowledge-Enhanced Language Models [31.209774088374374]
  This paper introduces TRELM, a Robust and Efficient Pre-training framework for Knowledge-Enhanced Language Models. We employ a robust approach to inject knowledge triples and employ a knowledge-augmented memory bank to capture valuable information. We show that TRELM reduces pre-training time by at least 50% and outperforms other KEPLMs in knowledge probing tasks and multiple knowledge-aware language understanding tasks.
  arXiv  Detail & Related papers  (2024-03-17T13:04:35Z)
• Interpreting and Improving Attention From the Perspective of Large Kernel Convolution [51.06461246235176]
  We introduce Large Kernel Convolutional Attention (LKCA), a novel formulation that reinterprets attention operations as a single large- Kernel convolution.<n>LKCA achieves competitive performance across various visual tasks, particularly in data-constrained settings.
  arXiv  Detail & Related papers  (2024-01-11T08:40:35Z)
• Self-Knowledge Guided Retrieval Augmentation for Large Language Models [59.771098292611846]
  Large language models (LLMs) have shown superior performance without task-specific fine-tuning. Retrieval-based methods can offer non-parametric world knowledge and improve the performance on tasks such as question answering. Self-Knowledge guided Retrieval augmentation (SKR) is a simple yet effective method which can let LLMs refer to the questions they have previously encountered.
  arXiv  Detail & Related papers  (2023-10-08T04:22:33Z)
• DefectHunter: A Novel LLM-Driven Boosted-Conformer-based Code Vulnerability Detection Mechanism [3.9377491512285157]
  DefectHunter is an innovative model for vulnerability identification that employs the Conformer mechanism. This mechanism fuses self-attention with convolutional networks to capture both local, position-wise features and global, content-based interactions.
  arXiv  Detail & Related papers  (2023-09-27T00:10:29Z)
• Thrust: Adaptively Propels Large Language Models with External Knowledge [58.72867916604562]
  Large-scale pre-trained language models (PTLMs) are shown to encode rich knowledge in their model parameters. The inherent knowledge in PTLMs can be opaque or static, making external knowledge necessary. We propose the instance-level adaptive propulsion of external knowledge (IAPEK), where we only conduct the retrieval when necessary.
  arXiv  Detail & Related papers  (2023-07-19T20:16:46Z)
• Knowledge Rumination for Pre-trained Language Models [77.55888291165462]
  We propose a new paradigm dubbed Knowledge Rumination to help the pre-trained language model utilize related latent knowledge without retrieving it from the external corpus. We apply the proposed knowledge rumination to various language models, including RoBERTa, DeBERTa, and GPT-3.
  arXiv  Detail & Related papers  (2023-05-15T15:47:09Z)
• LM-CORE: Language Models with Contextually Relevant External Knowledge [13.451001884972033]
  We argue that storing large amounts of knowledge in the model parameters is sub-optimal given the ever-growing amounts of knowledge and resource requirements. We present LM-CORE -- a general framework to achieve this -- that allows textitdecoupling of the language model training from the external knowledge source. Experimental results show that LM-CORE, having access to external knowledge, achieves significant and robust outperformance over state-of-the-art knowledge-enhanced language models on knowledge probing tasks.
  arXiv  Detail & Related papers  (2022-08-12T18:59:37Z)
• Human Parity on CommonsenseQA: Augmenting Self-Attention with External Attention [66.93307963324834]
  We propose to augment the transformer architecture with an external attention mechanism to bring external knowledge and context to bear. We find that the proposed external attention mechanism can significantly improve the performance of existing AI systems. The proposed system reaches human parity on the open CommonsenseQA research benchmark with an accuracy of 89.4% in comparison to the human accuracy of 88.9%.
  arXiv  Detail & Related papers  (2021-12-06T18:59:02Z)
• TRAIL: Near-Optimal Imitation Learning with Suboptimal Data [100.83688818427915]
  We present training objectives that use offline datasets to learn a factored transition model. Our theoretical analysis shows that the learned latent action space can boost the sample-efficiency of downstream imitation learning. To learn the latent action space in practice, we propose TRAIL (Transition-Reparametrized Actions for Imitation Learning), an algorithm that learns an energy-based transition model.
  arXiv  Detail & Related papers  (2021-10-27T21:05:00Z)
• Efficient training of lightweight neural networks using Online Self-Acquired Knowledge Distillation [51.66271681532262]
  Online Self-Acquired Knowledge Distillation (OSAKD) is proposed, aiming to improve the performance of any deep neural model in an online manner. We utilize k-nn non-parametric density estimation technique for estimating the unknown probability distributions of the data samples in the output feature space.
  arXiv  Detail & Related papers  (2021-08-26T14:01:04Z)
• Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Design [76.29738151117583]
  Federated Learning (FL) has become a promising tool for training effective machine learning models among distributed clients. However, low quality models could be uploaded to the aggregator server by unreliable clients, leading to a degradation or even a collapse of training. We model these unreliable behaviors of clients and propose a defensive mechanism to mitigate such a security risk.
  arXiv  Detail & Related papers  (2021-05-10T08:02:27Z)
• Incorporating Effective Global Information via Adaptive Gate Attention for Text Classification [13.45504908358177]
  We show that simple statistical information can enhance classification performance both efficiently and significantly compared with several baseline models. We propose a classifier with gate mechanism named Adaptive Gate Attention model with Global Information (AGA+GI) in which the adaptive gate mechanism incorporates global statistical features into latent semantic features. Our experiments show that the proposed method can achieve better accuracy than CNN-based and RNN-based approaches without global information on several benchmarks.
  arXiv  Detail & Related papers  (2020-02-22T10:06:37Z)

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.