Understanding the Interplay between Parametric and Contextual Knowledge for Large Language Models

• URL: http://arxiv.org/abs/2410.08414v1
• Date: Thu, 10 Oct 2024 23:09:08 GMT
• Title: Understanding the Interplay between Parametric and Contextual Knowledge for Large Language Models
• Authors: Sitao Cheng, Liangming Pan, Xunjian Yin, Xinyi Wang, William Yang Wang,
• Abstract summary: Large language models (LLMs) encode vast amounts of knowledge during pre-training. LLMs can be enhanced by incorporating contextual knowledge (CK) Can LLMs effectively integrate their internal PK with external CK to solve complex problems?
• Score: 85.13298925375692
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) encode vast amounts of knowledge during pre-training (parametric knowledge, or PK) and can further be enhanced by incorporating contextual knowledge (CK). Can LLMs effectively integrate their internal PK with external CK to solve complex problems? In this paper, we investigate the dynamic interaction between PK and CK, categorizing their relationships into four types: Supportive, Complementary, Conflicting, and Irrelevant. To support this investigation, we introduce ECHOQA, a benchmark spanning scientific, factual, and commonsense knowledge. Our results show that LLMs tend to suppress their PK when contextual information is available, even when it is complementary or irrelevant. While tailored instructions can encourage LLMs to rely more on their PK, they still struggle to fully leverage it. These findings reveal a key vulnerability in LLMs, raising concerns about their reliability in knowledge-intensive tasks. Resources are available at https://github.com/sitaocheng/Knowledge Interplay.

Related papers

• What External Knowledge is Preferred by LLMs? Characterizing and Exploring Chain of Evidence in Imperfect Context [19.78140793942713]
  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. 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.
  arXiv  Detail & Related papers  (2024-12-17T07:49:49Z)
• Towards Reliable Latent Knowledge Estimation in LLMs: Zero-Prompt Many-Shot Based Factual Knowledge Extraction [15.534647327246239]
  We propose to eliminate prompt engineering when probing large language models (LLMs) for factual knowledge. Our approach, called Zero-Prompt Latent Knowledge Estimator (ZP-LKE), leverages the in-context learning ability of LLMs. We perform a large-scale evaluation of the factual knowledge of a variety of open-source LLMs over a large set of relations and facts from the Wikidata knowledge base.
  arXiv  Detail & Related papers  (2024-04-19T15:40:39Z)
• LLMs' Reading Comprehension Is Affected by Parametric Knowledge and Struggles with Hypothetical Statements [59.71218039095155]
  Task of reading comprehension (RC) provides a primary means to assess language models' natural language understanding (NLU) capabilities. If the context aligns with the models' internal knowledge, it is hard to discern whether the models' answers stem from context comprehension or from internal information. To address this issue, we suggest to use RC on imaginary data, based on fictitious facts and entities.
  arXiv  Detail & Related papers  (2024-04-09T13:08:56Z)
• 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)
• "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)
• 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)
• Search-in-the-Chain: Interactively Enhancing Large Language Models with Search for Knowledge-intensive Tasks [121.74957524305283]
  This paper proposes a novel framework named textbfSearch-in-the-Chain (SearChain) for the interaction between Information Retrieval (IR) and Large Language Model (LLM) Experiments show that SearChain outperforms state-of-the-art baselines on complex knowledge-intensive tasks.
  arXiv  Detail & Related papers  (2023-04-28T10:15:25Z)

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.