Parameters vs. Context: Fine-Grained Control of Knowledge Reliance in Language Models

• URL: http://arxiv.org/abs/2503.15888v1
• Date: Thu, 20 Mar 2025 06:26:28 GMT
• Title: Parameters vs. Context: Fine-Grained Control of Knowledge Reliance in Language Models
• Abstract summary: Retrieval-Augmented Generation (RAG) mitigates hallucinations in Large Language Models (LLMs) by integrating external knowledge.<n>Conflicts between parametric knowledge and retrieved context pose challenges for RAGs.<n>We propose **CK-PLUG**, a plug-and-play method for controlling RAG's reliance on parametric and contextual knowledge.
• Score: 39.73834207174728
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval-Augmented Generation (RAG) mitigates hallucinations in Large Language Models (LLMs) by integrating external knowledge. However, conflicts between parametric knowledge and retrieved context pose challenges, particularly when retrieved information is unreliable or the model's internal knowledge is outdated. In such cases, LLMs struggle to determine whether to rely more on their own parameters or the conflicted context. To address this, we propose **CK-PLUG**, a plug-and-play method for controlling LLMs' reliance on parametric and contextual knowledge. We introduce a novel knowledge consistency metric, Confidence Gain, which detects knowledge conflicts by measuring entropy shifts in token probability distributions after context insertion. CK-PLUG then enables fine-grained control over knowledge preference by adjusting the probability distribution of tokens with negative confidence gain through a single tuning parameter. Experiments demonstrate CK-PLUG's ability to significantly regulate knowledge reliance in counterfactual RAG scenarios while maintaining generation fluency and knowledge accuracy. For instance, on Llama3-8B, memory recall (MR) of RAG response can be adjusted within a broad range (9.9%-71.9%), compared to the baseline of 42.1%. Moreover, CK-PLUG supports adaptive control based on the model's confidence in both internal and external knowledge, achieving consistent performance improvements across various general RAG tasks. Our code is available at: $\href{https://github.com/byronBBL/CK-PLUG}{\text{this https URL}}$.

Related papers

• SHIFT: Gate-Modulated Activation Steering for Knowledge Conflict Mitigation in Retrieval-Augmented Generation [45.618428862766365]
  Conflicts between retrieved context and parametric knowledge have emerged as a critical challenge in RAG systems.<n>We introduce SHIFT, a novel framework that reformulates neuron-level modification as learnable gate modulation.
  arXiv  Detail & Related papers  (2026-06-26T07:17:11Z)
• Trust or Abstain? A Self-Aware RAG Approach [45.1230482333942]
  Retrieval-augmented generation (RAG) improves large language models by incorporating external evidence.<n>Existing approaches mainly coordinate which source to use, without explicitly asking whether each answer path is correct.<n>We argue that faithful RAG requires LLM self-awareness, namely the ability to recognize the limits of its own knowledge and reasoning.
  arXiv  Detail & Related papers  (2026-05-11T05:44:27Z)
• Knowledge Capsules: Structured Nonparametric Memory Units for LLMs [0.6524460254566904]
  Large language models (LLMs) encode knowledge in parametric weights, making it costly to update or extend without retraining.<n>Retrieval-augmented generation (RAG) mitigates this limitation by appending retrieved text to the input, but operates purely through context expansion.<n>We propose Knowledge Capsules, structured nonparametric memory units that represent normalized relational knowledge.
  arXiv  Detail & Related papers  (2026-04-22T12:21:00Z)
• Fact-Checking with Large Language Models via Probabilistic Certainty and Consistency [7.806516365113592]
  Large language models (LLMs) are increasingly used in applications requiring factual accuracy.<n>While fact-checking can mitigate these errors, existing methods typically retrieve external evidence indiscriminately.<n>We introduce Probabilistic Certainty and Consistency (PCC), a framework that estimates factual confidence.
  arXiv  Detail & Related papers  (2026-01-05T21:57:41Z)
• Hindsight Distillation Reasoning with Knowledge Encouragement Preference for Knowledge-based Visual Question Answering [55.368681418311894]
  Existing Knowledge-based Visual Question Answering (KBVQA) methods either utilize implicit knowledge in multimodal large language models (MLLMs) via in-context learning or explicit knowledge via retrieval augmented generation.<n>We provide a Hindsight Distilled Reasoning (HinD) framework with Knowledge Encouragement Preference Optimization (KEPO)<n> Experiments on OK-VQA and A-OKVQA validate the effectiveness of HinD, showing that HinD with elicited reasoning from 7B-size MLLM achieves superior performance without commercial model APIs or outside knowledge.
  arXiv  Detail & Related papers  (2025-11-14T10:03:23Z)
• That's Deprecated! Understanding, Detecting, and Steering Knowledge Conflicts in Language Models for Code Generation [55.78914774437411]
  Large language models (LLMs) behave when faced with discrepancies between their parametric knowledge and conflicting information contained in a prompt.<n>We propose a domain-agnostic framework for constructing and interpreting such conflicts.<n>We show that activation-level steering can achieve up to a textbf12.6% improvement in steering success over a random baseline.
  arXiv  Detail & Related papers  (2025-10-21T22:27:56Z)
• Conflict-Aware Soft Prompting for Retrieval-Augmented Generation [13.671410389511498]
  Retrieval-augmented generation (RAG) enhances the capabilities of large language models (LLMs) by incorporating external knowledge into their input prompts.<n>RAG often fails to resolve the conflict between incorrect external context and correct parametric knowledge, known as context-memory conflict.<n>We introduce Conflict-Aware REtrieval-Augmented Generation (CARE), consisting of a context assessor and a base LLM.<n>CARE effectively mitigates context-memory conflicts, leading to an average performance gain of 5.0% on QA and fact-checking benchmarks.
  arXiv  Detail & Related papers  (2025-08-21T05:36:29Z)
• Can LLMs Detect Their Confabulations? Estimating Reliability in Uncertainty-Aware Language Models [24.72990207218907]
  Large Language Models (LLMs) are prone to generating fluent but incorrect content, known as confabulation.<n>We investigate how in-context information influences model behavior and whether LLMs can identify their unreliable responses.
  arXiv  Detail & Related papers  (2025-08-11T16:12:36Z)
• FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation [37.28571879699906]
  Large language models (LLMs) augmented with retrieval systems have demonstrated significant potential in handling knowledge-intensive tasks.<n>This paper proposes FaithfulRAG, a novel framework that resolves knowledge conflicts by explicitly modeling discrepancies between the models parametric knowledge and retrieved context.
  arXiv  Detail & Related papers  (2025-06-10T16:02:54Z)
• Knowledgeable-r1: Policy Optimization for Knowledge Exploration in Retrieval-Augmented Generation [6.870247946243668]
  Retrieval-augmented generation (RAG) is a mainstream method for improving performance on knowledge-intensive tasks.<n>We propose Knowledgeable-r1, that uses joint sampling and define multi policy distributions in knowledge capability exploration.<n> Experiments show that Knowledgeable-r1 significantly enhances robustness and reasoning accuracy in both parameters and contextual conflict tasks.
  arXiv  Detail & Related papers  (2025-06-05T15:34:15Z)
• Faithfulness-Aware Uncertainty Quantification for Fact-Checking the Output of Retrieval Augmented Generation [108.13261761812517]
  We introduce FRANQ (Faithfulness-based Retrieval Augmented UNcertainty Quantification), a novel method for hallucination detection in RAG outputs.<n>We present a new long-form Question Answering (QA) dataset annotated for both factuality and faithfulness.
  arXiv  Detail & Related papers  (2025-05-27T11:56:59Z)
• Accommodate Knowledge Conflicts in Retrieval-augmented LLMs: Towards Reliable Response Generation in the Wild [11.058848731627233]
  Large language models (LLMs) have advanced information retrieval systems. LLMs often face knowledge conflicts between internal memory and retrievaled external information. We propose Swin-VIB, a novel framework that integrates a pipeline of variational information bottleneck models into adaptive augmentation of retrieved information.
  arXiv  Detail & Related papers  (2025-04-17T14:40:31Z)
• PIP-KAG: Mitigating Knowledge Conflicts in Knowledge-Augmented Generation via Parametric Pruning [92.07119924043461]
  Knowledge-Augmented Generation (KAG) has shown great promise in updating the internal memory of Large Language Models (LLMs)<n>Current approaches to mitigating these conflicts mainly focus on improving external knowledge utilization.<n>We propose a ParametrIc Pruning-based Knowledge-Augmented Generation (PIP-KAG) approach, which prunes internal knowledge of LLMs.
  arXiv  Detail & Related papers  (2025-02-21T15:50:41Z)
• TrustRAG: Enhancing Robustness and Trustworthiness in RAG [31.231916859341865]
  TrustRAG is a framework that systematically filters compromised and irrelevant contents before they are retrieved for generation.<n>TrustRAG delivers substantial improvements in retrieval accuracy, efficiency, and attack resistance compared to existing approaches.
  arXiv  Detail & Related papers  (2025-01-01T15:57:34Z)
• To Trust or Not to Trust? Enhancing Large Language Models' Situated Faithfulness to External Contexts [10.748768620243982]
  Large Language Models (LLMs) are often augmented with external contexts, such as those used in retrieval-augmented generation (RAG)<n>We show that when provided with both correct and incorrect contexts, both open-source and proprietary models tend to overly rely on external information.<n>We propose two approaches: Self-Guided Confidence Reasoning (SCR) and Rule-Based Confidence Reasoning (RCR)
  arXiv  Detail & Related papers  (2024-10-18T17:59:47Z)
• Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models [20.605487145370752]
  We find that imperfect retrieval augmentation might be inevitable and quite harmful, through controlled analysis under realistic conditions. We propose Astute RAG, a novel RAG approach that adaptively elicits essential information from LLMs' internal knowledge. Further analysis reveals that Astute RAG effectively resolves knowledge conflicts, improving the reliability and trustworthiness of RAG systems.
  arXiv  Detail & Related papers  (2024-10-09T17:59:58Z)
• Retrieving, Rethinking and Revising: The Chain-of-Verification Can Improve Retrieval Augmented Generation [38.80878966092216]
  Recent Retrieval Augmented Generation (RAG) aims to enhance Large Language Models (LLMs) We propose the chain-of-verification (CoV-RAG) to enhance the external retrieval correctness and internal generation consistency.
  arXiv  Detail & Related papers  (2024-10-08T08:34:54Z)
• SFR-RAG: Towards Contextually Faithful LLMs [57.666165819196486]
  Retrieval Augmented Generation (RAG) is a paradigm that integrates external contextual information with large language models (LLMs) to enhance factual accuracy and relevance. We introduce SFR-RAG, a small LLM that is instruction-textual with an emphasis on context-grounded generation and hallucination. We also present ConBench, a new evaluation framework compiling multiple popular and diverse RAG benchmarks.
  arXiv  Detail & Related papers  (2024-09-16T01:08:18Z)
• Robust and Scalable Model Editing for Large Language Models [75.95623066605259]
  We propose EREN (Edit models by REading Notes) to improve the scalability and robustness of LLM editing. Unlike existing techniques, it can integrate knowledge from multiple edits, and correctly respond to syntactically similar but semantically unrelated inputs.
  arXiv  Detail & Related papers  (2024-03-26T06:57:23Z)
• ActiveRAG: Autonomously Knowledge Assimilation and Accommodation through Retrieval-Augmented Agents [49.30553350788524]
  Retrieval-Augmented Generation (RAG) enables Large Language Models (LLMs) to leverage external knowledge. Existing RAG models often treat LLMs as passive recipients of information. We introduce ActiveRAG, a multi-agent framework that mimics human learning behavior.
  arXiv  Detail & Related papers  (2024-02-21T06:04:53Z)
• R-Tuning: Instructing Large Language Models to Say `I Don't Know' [66.11375475253007]
  Large language models (LLMs) have revolutionized numerous domains with their impressive performance but still face their challenges. Previous instruction tuning methods force the model to complete a sentence no matter whether the model knows the knowledge or not. We present a new approach called Refusal-Aware Instruction Tuning (R-Tuning) Experimental results demonstrate R-Tuning effectively improves a model's ability to answer known questions and refrain from answering unknown questions.
  arXiv  Detail & Related papers  (2023-11-16T08:45:44Z)
• Seeking Neural Nuggets: Knowledge Transfer in Large Language Models from a Parametric Perspective [106.92016199403042]
  We empirically investigate knowledge transfer from larger to smaller models through a parametric perspective. We employ sensitivity-based techniques to extract and align knowledge-specific parameters between different large language models. Our findings highlight the critical factors contributing to the process of parametric knowledge transfer.
  arXiv  Detail & Related papers  (2023-10-17T17:58:34Z)

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.