Trustful LLMs: Customizing and Grounding Text Generation with Knowledge Bases and Dual Decoders

• URL: http://arxiv.org/abs/2411.07870v6
• Date: Fri, 20 Dec 2024 01:14:39 GMT
• Title: Trustful LLMs: Customizing and Grounding Text Generation with Knowledge Bases and Dual Decoders
• Authors: Xiaofeng Zhu, Jaya Krishna Mandivarapu,
• Abstract summary: We propose a post-processing algorithm that leverages knowledge triplets in RAG context to correct hallucinations.<n>We also propose a dual-decoder model that fuses RAG context to guide the generation process.
• Score: 5.929519489554968
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although people are impressed by the content generation skills of large language models, the use of LLMs, such as ChatGPT, is limited by the domain grounding of the content. The correctness and groundedness of the generated content need to be based on a verified context, such as results from Retrieval-Augmented Generation (RAG). One important issue when adapting LLMs to a customized domain is that the generated responses are often incomplete, or the additions are not verified and may even be hallucinated. Prior studies on hallucination detection have focused on evaluation metrics, which are not easily adaptable to dynamic domains and can be vulnerable to attacks like jail-breaking. In this work, we propose 1) a post-processing algorithm that leverages knowledge triplets in RAG context to correct hallucinations and 2) a dual-decoder model that fuses RAG context to guide the generation process.

Related papers

• CAAD: Context-Aware Adaptive Decoding for Truthful Text Generation [31.469511576774252]
  We propose a context-aware adaptive decoding method for large language models.<n>Our approach achieves a 2.8 percent average improvement on TruthfulQA.<n>Our model-agnostic, scalable, and efficient method requires only a single generation pass.
  arXiv  Detail & Related papers  (2025-08-04T08:28:25Z)
• LAQuer: Localized Attribution Queries in Content-grounded Generation [69.60308443863606]
  Grounded text generation models often produce content that deviates from their source material, requiring user verification to ensure accuracy.<n>Existing attribution methods associate entire sentences with source documents, which can be overwhelming for users seeking to fact-check specific claims.<n>We introduce Localized Attribution Queries (LAQuer), a new task that localizes selected spans of generated output to their corresponding source spans, allowing fine-grained and user-directed attribution.
  arXiv  Detail & Related papers  (2025-06-01T21:46:23Z)
• RoleRAG: Enhancing LLM Role-Playing via Graph Guided Retrieval [6.636092764694501]
  RoleRAG is a retrieval-based framework that integrates efficient entity disambiguation for knowledge indexing with a boundary-aware retriever for extracting contextually appropriate information from a structured knowledge graph.<n> Experiments on role-playing benchmarks show that RoleRAG's calibrated retrieval helps both general-purpose and role-specific LLMs better align with character knowledge and reduce hallucinated responses.
  arXiv  Detail & Related papers  (2025-05-24T06:11:17Z)
• Align-GRAG: Reasoning-Guided Dual Alignment for Graph Retrieval-Augmented Generation [75.9865035064794]
  Large language models (LLMs) have demonstrated remarkable capabilities, but still struggle with issues like hallucinations and outdated information.<n>Retrieval-augmented generation (RAG) addresses these issues by grounding LLM outputs in external knowledge with an Information Retrieval (IR) system.<n>We propose Align-GRAG, a novel reasoning-guided dual alignment framework in post-retrieval phrase.
  arXiv  Detail & Related papers  (2025-05-22T05:15:27Z)
• ResNetVLLM-2: Addressing ResNetVLLM's Multi-Modal Hallucinations [0.40964539027092917]
  Large Language Models (LLMs) have transformed natural language processing (NLP) tasks, but they suffer from hallucination, generating plausible yet factually incorrect content. This issue extends to Video-Language Models (VideoLLMs), where textual descriptions may inaccurately represent visual content, resulting in multi-modal hallucinations. We introduce a two-step protocol: (1) a faithfulness detection strategy that uses a modified Lynx model to assess semantic alignment between generated captions and ground-truth video references, and (2) a hallucination mitigation strategy using Retrieval-Augmented Generation (RAG) with an ad-hoc knowledge base dynamically constructed during inference.
  arXiv  Detail & Related papers  (2025-04-20T00:10:44Z)
• Systematic Knowledge Injection into Large Language Models via Diverse Augmentation for Domain-Specific RAG [24.660769275714685]
  Retrieval-Augmented Generation (RAG) has emerged as a prominent method for incorporating domain knowledge into Large Language Models (LLMs) We present a novel framework that significantly enhances the fine-tuning process by augmenting the training data in two ways -- context augmentation and knowledge paraphrasing.
  arXiv  Detail & Related papers  (2025-02-12T12:39:51Z)
• Towards Copyright Protection for Knowledge Bases of Retrieval-augmented Language Models via Reasoning [58.57194301645823]
  Large language models (LLMs) are increasingly integrated into real-world personalized applications.<n>The valuable and often proprietary nature of the knowledge bases used in RAG introduces the risk of unauthorized usage by adversaries.<n>Existing methods that can be generalized as watermarking techniques to protect these knowledge bases typically involve poisoning or backdoor attacks.<n>We propose name for harmless' copyright protection of knowledge bases.
  arXiv  Detail & Related papers  (2025-02-10T09:15:56Z)
• Harnessing Large Language Models for Knowledge Graph Question Answering via Adaptive Multi-Aspect Retrieval-Augmentation [81.18701211912779]
  We introduce an Adaptive Multi-Aspect Retrieval-augmented over KGs (Amar) framework. This method retrieves knowledge including entities, relations, and subgraphs, and converts each piece of retrieved text into prompt embeddings. Our method has achieved state-of-the-art performance on two common datasets.
  arXiv  Detail & Related papers  (2024-12-24T16:38:04Z)
• Invar-RAG: Invariant LLM-aligned Retrieval for Better Generation [43.630437906898635]
  We propose a novel two-stage fine-tuning architecture called Invar-RAG. In the retrieval stage, an LLM-based retriever is constructed by integrating LoRA-based representation learning. In the generation stage, a refined fine-tuning method is employed to improve LLM accuracy in generating answers based on retrieved information.
  arXiv  Detail & Related papers  (2024-11-11T14:25:37Z)
• SimRAG: Self-Improving Retrieval-Augmented Generation for Adapting Large Language Models to Specialized Domains [45.349645606978434]
  Retrieval-augmented generation (RAG) enhances the question-answering abilities of large language models (LLMs) However, adapting general-purpose RAG systems to specialized fields such as science and medicine poses unique challenges due to distribution shifts and limited access to domain-specific data. We propose SimRAG, a self-training approach that equips the LLM with joint capabilities of question answering and question generation for domain adaptation.
  arXiv  Detail & Related papers  (2024-10-23T15:24:16Z)
• Domain-Specific Retrieval-Augmented Generation Using Vector Stores, Knowledge Graphs, and Tensor Factorization [7.522493227357079]
  Large Language Models (LLMs) are pre-trained on large-scale corpora. LLMs suffer from hallucinations, knowledge cut-offs, and lack of knowledge attributions. We introduce SMART-SLIC, a highly domain-specific LLM framework.
  arXiv  Detail & Related papers  (2024-10-03T17:40:55Z)
• GEM-RAG: Graphical Eigen Memories For Retrieval Augmented Generation [3.2027710059627545]
  We introduce Graphical Eigen Memories For Retrieval Augmented Generation (GEM-RAG) GEM-RAG works by tagging each chunk of text in a given text corpus with LLM generated utility'' questions. We evaluate GEM-RAG, using both UnifiedQA and GPT-3.5 Turbo as the LLMs, with SBERT, and OpenAI's text encoders on two standard QA tasks.
  arXiv  Detail & Related papers  (2024-09-23T21:42:47Z)
• Ground Every Sentence: Improving Retrieval-Augmented LLMs with Interleaved Reference-Claim Generation [51.8188846284153]
  RAG has been widely adopted to enhance Large Language Models (LLMs) Attributed Text Generation (ATG) has attracted growing attention, which provides citations to support the model's responses in RAG. This paper proposes a fine-grained ATG method called ReClaim(Refer & Claim), which alternates the generation of references and answers step by step.
  arXiv  Detail & Related papers  (2024-07-01T20:47:47Z)
• Enhancing Contextual Understanding in Large Language Models through Contrastive Decoding [9.2433070542025]
  Large language models (LLMs) tend to inadequately integrate input context during text generation. We introduce a novel approach integrating contrastive decoding with adversarial irrelevant passages as negative samples.
  arXiv  Detail & Related papers  (2024-05-04T20:38:41Z)
• DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text [73.68051228972024]
  Large Language Models (LLMs) have exhibited impressive generation capabilities, but they suffer from hallucinations when relying on their internal knowledge. Retrieval-augmented LLMs have emerged as a potential solution to ground LLMs in external knowledge.
  arXiv  Detail & Related papers  (2023-10-31T04:37:57Z)
• Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection [74.51523859064802]
  We introduce a new framework called Self-Reflective Retrieval-Augmented Generation (Self-RAG) Self-RAG enhances an LM's quality and factuality through retrieval and self-reflection. It significantly outperforms state-of-the-art LLMs and retrieval-augmented models on a diverse set of tasks.
  arXiv  Detail & Related papers  (2023-10-17T18:18:32Z)
• DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models [79.01926242857613]
  Large language models (LLMs) are prone to hallucinations, generating content that deviates from facts seen during pretraining. We propose a simple decoding strategy for reducing hallucinations with pretrained LLMs. We find that this Decoding by Contrasting Layers (DoLa) approach is able to better surface factual knowledge and reduce the generation of incorrect facts.
  arXiv  Detail & Related papers  (2023-09-07T17:45:31Z)
• Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback [127.75419038610455]
  Large language models (LLMs) are able to generate human-like, fluent responses for many downstream tasks. This paper proposes a LLM-Augmenter system, which augments a black-box LLM with a set of plug-and-play modules.
  arXiv  Detail & Related papers  (2023-02-24T18:48:43Z)
• MuRAG: Multimodal Retrieval-Augmented Generator for Open Question Answering over Images and Text [58.655375327681774]
  We propose the first Multimodal Retrieval-Augmented Transformer (MuRAG) MuRAG accesses an external non-parametric multimodal memory to augment language generation. Our results show that MuRAG achieves state-of-the-art accuracy, outperforming existing models by 10-20% absolute on both datasets.
  arXiv  Detail & Related papers  (2022-10-06T13:58:03Z)

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.