What External Knowledge is Preferred by LLMs? Characterizing and Exploring Chain of Evidence in Imperfect Context
- URL: http://arxiv.org/abs/2412.12632v1
- Date: Tue, 17 Dec 2024 07:49:49 GMT
- Title: What External Knowledge is Preferred by LLMs? Characterizing and Exploring Chain of Evidence in Imperfect Context
- Authors: Zhiyuan Chang, Mingyang Li, Xiaojun Jia, Junjie Wang, Yuekai Huang, Qing Wang, Yihao Huang, Yang Liu,
- Abstract summary: This paper focuses on LLMs' preferred external knowledge in imperfect contexts when handling multi-hop QA.<n>Inspired by criminal procedural law's Chain of Evidence (CoE), we characterize that knowledge preferred by LLMs should maintain both relevance to the question and mutual support among knowledge pieces.<n>We propose an automated CoE discrimination approach and explore LLMs' preferences from their effectiveness, faithfulness and robustness, as well as CoE's usability in a naive Retrieval-Augmented Generation (RAG) case.
- Score: 19.78140793942713
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Incorporating external knowledge into large language models (LLMs) has emerged as a promising approach to mitigate outdated knowledge and hallucination in LLMs. However, external knowledge is often imperfect. In addition to useful knowledge, external knowledge is rich in irrelevant or misinformation in the context that can impair the reliability of LLM responses. This paper focuses on LLMs' preferred external knowledge in imperfect contexts when handling multi-hop QA. Inspired by criminal procedural law's Chain of Evidence (CoE), we characterize that knowledge preferred by LLMs should maintain both relevance to the question and mutual support among knowledge pieces. Accordingly, we propose an automated CoE discrimination approach and explore LLMs' preferences from their effectiveness, faithfulness and robustness, as well as CoE's usability in a naive Retrieval-Augmented Generation (RAG) case. The evaluation on five LLMs reveals that CoE enhances LLMs through more accurate generation, stronger answer faithfulness, better robustness against knowledge conflict, and improved performance in a popular RAG case.
Related papers
- LLM Inference Enhanced by External Knowledge: A Survey [16.319049759753106]
This study explores strategies for using external knowledge to enhance large language models (LLMs)<n>Our comparative analysis highlights the trade-offs among interpretability, scalability, and performance.
arXiv Detail & Related papers (2025-05-30T09:08:51Z) - 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)
Current approaches to mitigating these conflicts mainly focus on improving external knowledge utilization.
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) - Decoding Knowledge in Large Language Models: A Framework for Categorization and Comprehension [14.039653386385519]
Large language models (LLMs) acquire, retain, and apply knowledge.
This paper introduces a novel framework, K-(CSA)2, which categorizes LLM knowledge along two dimensions: correctness and confidence.
arXiv Detail & Related papers (2025-01-02T16:34:10Z) - Internal and External Knowledge Interactive Refinement Framework for Knowledge-Intensive Question Answering [33.89176174108559]
We propose a new internal and external knowledge interactive refinement paradigm dubbed IEKR.
By simply adding a prompt like 'Tell me something about' to the LLMs, we try to review related explicit knowledge and insert them with the query into the retriever for external retrieval.
arXiv Detail & Related papers (2024-08-23T10:52:57Z) - Large Language Models are Limited in Out-of-Context Knowledge Reasoning [65.72847298578071]
Large Language Models (LLMs) possess extensive knowledge and strong capabilities in performing in-context reasoning.
This paper focuses on a significant aspect of out-of-context reasoning: Out-of-Context Knowledge Reasoning (OCKR), which is to combine multiple knowledge to infer new knowledge.
arXiv Detail & Related papers (2024-06-11T15:58:59Z) - Evaluating the External and Parametric Knowledge Fusion of Large Language Models [72.40026897037814]
We develop a systematic pipeline for data construction and knowledge infusion to simulate knowledge fusion scenarios.
Our investigation reveals that enhancing parametric knowledge within LLMs can significantly bolster their capability for knowledge integration.
Our findings aim to steer future explorations on harmonizing external and parametric knowledge within LLMs.
arXiv Detail & Related papers (2024-05-29T11:48:27Z) - Untangle the KNOT: Interweaving Conflicting Knowledge and Reasoning Skills in Large Language Models [51.72963030032491]
Knowledge documents for large language models (LLMs) may conflict with the memory of LLMs due to outdated or incorrect knowledge.
We construct a new dataset, dubbed KNOT, for knowledge conflict resolution examination in the form of question answering.
arXiv Detail & Related papers (2024-04-04T16:40:11Z) - TrustScore: Reference-Free Evaluation of LLM Response Trustworthiness [58.721012475577716]
Large Language Models (LLMs) have demonstrated impressive capabilities across various domains, prompting a surge in their practical applications.
This paper introduces TrustScore, a framework based on the concept of Behavioral Consistency, which evaluates whether an LLMs response aligns with its intrinsic knowledge.
arXiv Detail & Related papers (2024-02-19T21:12:14Z) - 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) - When Do LLMs Need Retrieval Augmentation? Mitigating LLMs' Overconfidence Helps Retrieval Augmentation [66.01754585188739]
Large Language Models (LLMs) have been found to have difficulty knowing they do not possess certain knowledge.
Retrieval Augmentation (RA) has been extensively studied to mitigate LLMs' hallucinations.
We propose several methods to enhance LLMs' perception of knowledge boundaries and show that they are effective in reducing overconfidence.
arXiv Detail & Related papers (2024-02-18T04:57:19Z) - KnowTuning: Knowledge-aware Fine-tuning for Large Language Models [83.5849717262019]
We propose a knowledge-aware fine-tuning (KnowTuning) method to improve fine-grained and coarse-grained knowledge awareness of LLMs.
KnowTuning generates more facts with less factual error rate under fine-grained facts evaluation.
arXiv Detail & Related papers (2024-02-17T02:54:32Z) - Knowledge Verification to Nip Hallucination in the Bud [69.79051730580014]
We demonstrate the feasibility of mitigating hallucinations by verifying and minimizing the inconsistency between external knowledge present in the alignment data and the intrinsic knowledge embedded within foundation LLMs.
We propose a novel approach called Knowledge Consistent Alignment (KCA), which employs a well-aligned LLM to automatically formulate assessments based on external knowledge.
We demonstrate the superior efficacy of KCA in reducing hallucinations across six benchmarks, utilizing foundation LLMs of varying backbones and scales.
arXiv Detail & Related papers (2024-01-19T15:39:49Z) - RECALL: A Benchmark for LLMs Robustness against External Counterfactual
Knowledge [69.79676144482792]
This study aims to evaluate the ability of LLMs to distinguish reliable information from external knowledge.
Our benchmark consists of two tasks, Question Answering and Text Generation, and for each task, we provide models with a context containing counterfactual information.
arXiv Detail & Related papers (2023-11-14T13:24:19Z) - "Merge Conflicts!" Exploring the Impacts of External Distractors to
Parametric Knowledge Graphs [15.660128743249611]
Large language models (LLMs) acquire extensive knowledge during pre-training, known as their parametric knowledge.
LLMs inevitably require external knowledge during their interactions with users.
This raises a crucial question: How will LLMs respond when external knowledge interferes with their parametric knowledge?
arXiv Detail & Related papers (2023-09-15T17:47:59Z) - 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.