Knowledge Localization: Mission Not Accomplished? Enter Query Localization!

Yuheng Chen, Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao·May 23, 2024

Summary

The paper challenges the Knowledge Localization (KL) assumption in large language models, proposing the Query Localization (QL) hypothesis. It divides knowledge into KI (consistent) and KII (inconsistent) categories, with KII being query-dependent. The authors propose a Consistency-Aware KN modification method to improve knowledge modification and conduct experiments using GPT-2, LLaMA2-7b, and LLaMA3-8b models. Results show that a significant proportion of factual knowledge does not adhere to the KL assumption, and KII knowledge is more prevalent in larger models. The study employs statistical analysis, consistency analysis, and experiments to demonstrate the dynamic nature of knowledge selection in response to queries, with the QL assumption supporting better performance and generalization.

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to investigate the Knowledge Localization (KL) assumption and proposes the Query Localization (QL) assumption as a solution . This paper addresses the issue of whether factual knowledge can be localized to specific knowledge neurons, challenging the existing KL assumption and introducing the QL assumption as an alternative approach. The QL assumption includes Query-KN Mapping and Dynamic KN Selection, providing a new perspective on how factual knowledge is processed in large language models . This problem is novel as it re-evaluates the traditional understanding of knowledge localization and introduces a fresh concept to enhance the understanding of how factual knowledge is represented and utilized in language models.


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the Query Localization (QL) assumption as a more realistic hypothesis compared to the Knowledge Localization (KL) assumption in the context of knowledge neurons (KN) and large language models (LLMs) . The QL assumption includes two key aspects: Query-KN Mapping and Dynamic KN Selection, which aim to improve knowledge modification methods and enhance the performance of PLMs . The study rigorously explores the limitations of the KL assumption and provides evidence supporting the validity and effectiveness of the QL assumption in understanding how factual knowledge is stored and expressed in PLMs .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes several new ideas, methods, and models related to knowledge localization and editing in large language models:

  • Query Localization (QL) Assumption: The paper introduces the Query Localization assumption, which includes Query-KN Mapping and Dynamic KN Selection. This assumption aims to improve knowledge modification methods by rewarding high activation values and penalizing low consistency knowledge neurons for editing .
  • Consistency-Aware KN Modification Method: Based on the QL assumption, the paper presents a Consistency-Aware KN modification method. This method outperforms baselines by an average of 8% and 9% in the "Erasure" setting on LLaMA3-8b, demonstrating its effectiveness in knowledge editing .
  • In-depth Exploration of Knowledge Localization: The paper conducts a thorough investigation of the Knowledge Localization assumption, highlighting that factual knowledge often does not conform to this assumption from statistical and knowledge modification perspectives .
  • Experimental Validation: The research includes 39 sets of experiments across 13 experimental settings on 3 Pre-trained Language Models (PLMs), supported by additional visualization experiments to ensure the rigor of the conclusions .
  • Identification of Knowledge Integration Issues: The paper introduces the concept of Knowledge Integration Issues (KII), referring to knowledge that does not align with the knowledge localization assumption. This term is explored through experiments using various auto-regressive models like GPT-2, LLaMA2-7b, and LLaMA3-8b, showcasing the scalability of the methods and conclusions .
  • Proposed Solutions: The paper not only identifies limitations in existing knowledge editing methods but also offers solutions to address these issues, providing a comprehensive analysis of the challenges and potential improvements in knowledge localization and editing in large language models . The paper introduces novel characteristics and advantages compared to previous methods in the field of knowledge localization and editing in large language models:
  • Query Localization (QL) Assumption: The paper proposes the Query Localization assumption, which includes Query-KN Mapping and Dynamic KN Selection. This assumption aims to enhance knowledge modification methods by rewarding high activation values and penalizing low consistency knowledge neurons for editing .
  • Consistency-Aware KN Modification Method: Based on the QL assumption, the paper presents a Consistency-Aware KN modification method that outperforms baselines by an average of 8% and 9% in the "Erasure" setting on LLaMA3-8b, demonstrating its effectiveness in knowledge editing .
  • In-depth Exploration of Knowledge Localization: The research conducts a thorough investigation of the Knowledge Localization assumption, highlighting that factual knowledge often does not conform to this assumption from statistical and knowledge modification perspectives .
  • Experimental Validation: The study includes 39 sets of experiments across 13 experimental settings on 3 Pre-trained Language Models (PLMs), supported by additional visualization experiments to ensure the rigor of the conclusions .
  • Identification of Knowledge Integration Issues: The paper introduces the concept of Knowledge Integration Issues (KII), referring to knowledge that does not align with the knowledge localization assumption. This term is explored through experiments using various auto-regressive models like GPT-2, LLaMA2-7b, and LLaMA3-8b, showcasing the scalability of the methods and conclusions .
  • Proposed Solutions: The paper not only identifies limitations in existing knowledge editing methods but also offers solutions to address these issues, providing a comprehensive analysis of the challenges and potential improvements in knowledge localization and editing in large language models .

Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related researches exist in the field of knowledge localization. Noteworthy researchers in this area include Yuheng Chen, Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao, and many others . The key to the solution mentioned in the paper involves re-examining the Knowledge Localization (KL) assumption and proposing the Query Localization (QL) assumption, which includes Query-KN Mapping and Dynamic KN Selection. Extensive experiments validate the QL assumption and suggest that the KL assumption is essentially a simplification of the QL assumption .


How were the experiments in the paper designed?

The experiments in the paper were designed with specific methodologies and parameters:

  • The experiments involved acquiring knowledge neurons, which computed the activation values of all neurons for models like GPT-2, LLaMA2-7b, and LLaMA3-8b, with varying time requirements for acquiring knowledge neurons .
  • The experiments utilized a multi-GPU distributed processing setup, with the total time spent on acquiring knowledge neurons being approximately 26 days .
  • The experimental design included setting thresholds for different experimental settings, such as for GPT2, Dai et al., Enguehard, and Chen et al., with specific threshold values for each setting .
  • The experiments also involved consistency analysis, knowledge modification, and obtaining knowledge synapses, each with their own experimental hyperparameters and settings .
  • The paper conducted extensive experiments to validate the Query Localization (QL) assumption, which includes Query-KN Mapping and Dynamic KN Selection, to re-examine the Knowledge Localization (KL) assumption .
  • Future work suggested in the paper could delve into exploring the reasons behind the existence of different types of knowledge neurons (KII) and how to leverage the Query Localization assumption to enhance model editing methods .

What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the ParaRel dataset, which is a high-quality resource of cloze-style query English paraphrases . The code for the study is not explicitly mentioned to be open source in the provided context .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The study explores the Knowledge Localization (KL) assumption and introduces the Query Localization (QL) assumption, which includes Query-KN Mapping and Dynamic KN Selection . Through various experiments, the authors demonstrate the existence of Knowledge Inconsistency (KII) and the limitations of the KL assumption . The experiments involve knowledge modification methods, such as Erasure and Update, to classify facts into Knowledge Inconsistency (KII) and Knowledge Inconsistency (KI) . The findings consistently show a high ratio of KII and low Consistency Scores (CS), indicating a significant difference between KI and KII . Additionally, the study conducts 39 sets of experiments across 13 experimental settings on 3 Pretrained Language Models (PLMs) to validate the conclusions rigorously . The proposed Consistency-Aware KN modification method outperforms baselines, providing further evidence for the validity of the Query Localization assumption .


What are the contributions of this paper?

The contributions of the paper "Knowledge Localization: Mission Not Accomplished? Enter Query Localization!" include:

  • Conducting an in-depth exploration of the Knowledge Localization assumption, showing that knowledge not conforming to this assumption is prevalent from statistical and knowledge modification perspectives .
  • Proposing a more realistic Query Localization assumption, which includes Query-KN Mapping and Dynamic KN Selection. Introducing a Consistency-Aware KN modification method that outperforms baselines in the "Erasure" setting on LLaMA3-8b .
  • Conducting 39 sets of experiments across 13 experimental settings on 3 PLMs, supplemented by additional visualization experiments to ensure the rigor of the conclusions .
  • Investigating the limitations of Knowledge Localization and demonstrating the existence of KII, which refers to knowledge that does not satisfy the knowledge localization assumption. The experiments used GPT-2, LLaMA2-7b, and LLaMA3-8b to assess the scalability of the methods and conclusions .

What work can be continued in depth?

To delve deeper into the research on large language models, there are several avenues for further exploration:

  • Understanding neural circuit function through synaptic engineering: This involves studying how neural circuits function through synaptic engineering, which can provide insights into the inner workings of language models .
  • Identifying semantic induction heads for in-context learning: Exploring semantic induction heads can enhance the understanding of how language models learn in context, contributing to advancements in model comprehension .
  • Investigating language-specific neurons for multilingual capabilities: Researching language-specific neurons can unlock the key to the multilingual capabilities of large language models, offering valuable insights into their linguistic processing mechanisms .
  • Measuring and improving consistency in pretrained language models: Further studies on measuring and enhancing consistency in pretrained language models can lead to advancements in model performance and reliability .
  • Dissecting recall of factual associations in auto-regressive language models: Delving into the recall of factual associations in auto-regressive language models can provide a deeper understanding of how these models retrieve and utilize information, contributing to improvements in their functionality .

Introduction
Background
[A. Overview of Knowledge Localization (KL) in LLMs]
[B. Limitations of KL assumption]
Objective
[1. To challenge the KL assumption]
[2. Introduce Query Localization (QL) hypothesis]
Method
Data Collection
[A. Selection of Models: GPT-2, LLaMA2-7b, LLaMA3-8b]
[B. Dataset Selection: Factual Knowledge Datasets]
Data Preprocessing
[1. Identifying KI and KII Knowledge]
[2. Consistency Analysis of Knowledge Types]
Consistency-Aware Knowledge Modification (CKM) Method
[A. Development of the method]
[B. Implementation on LLM models]
Experimental Design
[1. Controlled experiments with varying queries]
[2. Comparison with KL-based approaches]
Results and Analysis
Statistical Analysis
[1. Proportion of factual knowledge in KI and KII]
[2. Size effect on KII prevalence]
Consistency Evaluation
[A. Query-dependent knowledge in KII]
[B. Impact on model performance]
Generalization and Performance
[1. QL hypothesis and better performance]
[2. Improved generalization with CKM]
Conclusion
[1. Relevance of QL hypothesis]
[2. Implications for LLM development]
[3. Future directions for research]
References
[Citation list of relevant literature]
Basic info
papers
computation and language
artificial intelligence
Advanced features
Insights
What method does the authors suggest for improving knowledge modification?
How do the results of the experiments with GPT-2, LLaMA2-7b, and LLaMA3-8b models support the QL hypothesis?
What are the two categories of knowledge proposed in the paper?
What does the paper challenge in large language models?

Knowledge Localization: Mission Not Accomplished? Enter Query Localization!

Yuheng Chen, Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao·May 23, 2024

Summary

The paper challenges the Knowledge Localization (KL) assumption in large language models, proposing the Query Localization (QL) hypothesis. It divides knowledge into KI (consistent) and KII (inconsistent) categories, with KII being query-dependent. The authors propose a Consistency-Aware KN modification method to improve knowledge modification and conduct experiments using GPT-2, LLaMA2-7b, and LLaMA3-8b models. Results show that a significant proportion of factual knowledge does not adhere to the KL assumption, and KII knowledge is more prevalent in larger models. The study employs statistical analysis, consistency analysis, and experiments to demonstrate the dynamic nature of knowledge selection in response to queries, with the QL assumption supporting better performance and generalization.
Mind map
[2. Improved generalization with CKM]
[1. QL hypothesis and better performance]
[B. Impact on model performance]
[A. Query-dependent knowledge in KII]
[2. Size effect on KII prevalence]
[1. Proportion of factual knowledge in KI and KII]
[2. Comparison with KL-based approaches]
[1. Controlled experiments with varying queries]
[B. Implementation on LLM models]
[A. Development of the method]
[2. Consistency Analysis of Knowledge Types]
[1. Identifying KI and KII Knowledge]
[B. Dataset Selection: Factual Knowledge Datasets]
[A. Selection of Models: GPT-2, LLaMA2-7b, LLaMA3-8b]
[2. Introduce Query Localization (QL) hypothesis]
[1. To challenge the KL assumption]
[B. Limitations of KL assumption]
[A. Overview of Knowledge Localization (KL) in LLMs]
[Citation list of relevant literature]
[3. Future directions for research]
[2. Implications for LLM development]
[1. Relevance of QL hypothesis]
Generalization and Performance
Consistency Evaluation
Statistical Analysis
Experimental Design
Consistency-Aware Knowledge Modification (CKM) Method
Data Preprocessing
Data Collection
Objective
Background
References
Conclusion
Results and Analysis
Method
Introduction
Outline
Introduction
Background
[A. Overview of Knowledge Localization (KL) in LLMs]
[B. Limitations of KL assumption]
Objective
[1. To challenge the KL assumption]
[2. Introduce Query Localization (QL) hypothesis]
Method
Data Collection
[A. Selection of Models: GPT-2, LLaMA2-7b, LLaMA3-8b]
[B. Dataset Selection: Factual Knowledge Datasets]
Data Preprocessing
[1. Identifying KI and KII Knowledge]
[2. Consistency Analysis of Knowledge Types]
Consistency-Aware Knowledge Modification (CKM) Method
[A. Development of the method]
[B. Implementation on LLM models]
Experimental Design
[1. Controlled experiments with varying queries]
[2. Comparison with KL-based approaches]
Results and Analysis
Statistical Analysis
[1. Proportion of factual knowledge in KI and KII]
[2. Size effect on KII prevalence]
Consistency Evaluation
[A. Query-dependent knowledge in KII]
[B. Impact on model performance]
Generalization and Performance
[1. QL hypothesis and better performance]
[2. Improved generalization with CKM]
Conclusion
[1. Relevance of QL hypothesis]
[2. Implications for LLM development]
[3. Future directions for research]
References
[Citation list of relevant literature]

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to investigate the Knowledge Localization (KL) assumption and proposes the Query Localization (QL) assumption as a solution . This paper addresses the issue of whether factual knowledge can be localized to specific knowledge neurons, challenging the existing KL assumption and introducing the QL assumption as an alternative approach. The QL assumption includes Query-KN Mapping and Dynamic KN Selection, providing a new perspective on how factual knowledge is processed in large language models . This problem is novel as it re-evaluates the traditional understanding of knowledge localization and introduces a fresh concept to enhance the understanding of how factual knowledge is represented and utilized in language models.


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the Query Localization (QL) assumption as a more realistic hypothesis compared to the Knowledge Localization (KL) assumption in the context of knowledge neurons (KN) and large language models (LLMs) . The QL assumption includes two key aspects: Query-KN Mapping and Dynamic KN Selection, which aim to improve knowledge modification methods and enhance the performance of PLMs . The study rigorously explores the limitations of the KL assumption and provides evidence supporting the validity and effectiveness of the QL assumption in understanding how factual knowledge is stored and expressed in PLMs .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes several new ideas, methods, and models related to knowledge localization and editing in large language models:

  • Query Localization (QL) Assumption: The paper introduces the Query Localization assumption, which includes Query-KN Mapping and Dynamic KN Selection. This assumption aims to improve knowledge modification methods by rewarding high activation values and penalizing low consistency knowledge neurons for editing .
  • Consistency-Aware KN Modification Method: Based on the QL assumption, the paper presents a Consistency-Aware KN modification method. This method outperforms baselines by an average of 8% and 9% in the "Erasure" setting on LLaMA3-8b, demonstrating its effectiveness in knowledge editing .
  • In-depth Exploration of Knowledge Localization: The paper conducts a thorough investigation of the Knowledge Localization assumption, highlighting that factual knowledge often does not conform to this assumption from statistical and knowledge modification perspectives .
  • Experimental Validation: The research includes 39 sets of experiments across 13 experimental settings on 3 Pre-trained Language Models (PLMs), supported by additional visualization experiments to ensure the rigor of the conclusions .
  • Identification of Knowledge Integration Issues: The paper introduces the concept of Knowledge Integration Issues (KII), referring to knowledge that does not align with the knowledge localization assumption. This term is explored through experiments using various auto-regressive models like GPT-2, LLaMA2-7b, and LLaMA3-8b, showcasing the scalability of the methods and conclusions .
  • Proposed Solutions: The paper not only identifies limitations in existing knowledge editing methods but also offers solutions to address these issues, providing a comprehensive analysis of the challenges and potential improvements in knowledge localization and editing in large language models . The paper introduces novel characteristics and advantages compared to previous methods in the field of knowledge localization and editing in large language models:
  • Query Localization (QL) Assumption: The paper proposes the Query Localization assumption, which includes Query-KN Mapping and Dynamic KN Selection. This assumption aims to enhance knowledge modification methods by rewarding high activation values and penalizing low consistency knowledge neurons for editing .
  • Consistency-Aware KN Modification Method: Based on the QL assumption, the paper presents a Consistency-Aware KN modification method that outperforms baselines by an average of 8% and 9% in the "Erasure" setting on LLaMA3-8b, demonstrating its effectiveness in knowledge editing .
  • In-depth Exploration of Knowledge Localization: The research conducts a thorough investigation of the Knowledge Localization assumption, highlighting that factual knowledge often does not conform to this assumption from statistical and knowledge modification perspectives .
  • Experimental Validation: The study includes 39 sets of experiments across 13 experimental settings on 3 Pre-trained Language Models (PLMs), supported by additional visualization experiments to ensure the rigor of the conclusions .
  • Identification of Knowledge Integration Issues: The paper introduces the concept of Knowledge Integration Issues (KII), referring to knowledge that does not align with the knowledge localization assumption. This term is explored through experiments using various auto-regressive models like GPT-2, LLaMA2-7b, and LLaMA3-8b, showcasing the scalability of the methods and conclusions .
  • Proposed Solutions: The paper not only identifies limitations in existing knowledge editing methods but also offers solutions to address these issues, providing a comprehensive analysis of the challenges and potential improvements in knowledge localization and editing in large language models .

Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related researches exist in the field of knowledge localization. Noteworthy researchers in this area include Yuheng Chen, Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao, and many others . The key to the solution mentioned in the paper involves re-examining the Knowledge Localization (KL) assumption and proposing the Query Localization (QL) assumption, which includes Query-KN Mapping and Dynamic KN Selection. Extensive experiments validate the QL assumption and suggest that the KL assumption is essentially a simplification of the QL assumption .


How were the experiments in the paper designed?

The experiments in the paper were designed with specific methodologies and parameters:

  • The experiments involved acquiring knowledge neurons, which computed the activation values of all neurons for models like GPT-2, LLaMA2-7b, and LLaMA3-8b, with varying time requirements for acquiring knowledge neurons .
  • The experiments utilized a multi-GPU distributed processing setup, with the total time spent on acquiring knowledge neurons being approximately 26 days .
  • The experimental design included setting thresholds for different experimental settings, such as for GPT2, Dai et al., Enguehard, and Chen et al., with specific threshold values for each setting .
  • The experiments also involved consistency analysis, knowledge modification, and obtaining knowledge synapses, each with their own experimental hyperparameters and settings .
  • The paper conducted extensive experiments to validate the Query Localization (QL) assumption, which includes Query-KN Mapping and Dynamic KN Selection, to re-examine the Knowledge Localization (KL) assumption .
  • Future work suggested in the paper could delve into exploring the reasons behind the existence of different types of knowledge neurons (KII) and how to leverage the Query Localization assumption to enhance model editing methods .

What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the ParaRel dataset, which is a high-quality resource of cloze-style query English paraphrases . The code for the study is not explicitly mentioned to be open source in the provided context .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The study explores the Knowledge Localization (KL) assumption and introduces the Query Localization (QL) assumption, which includes Query-KN Mapping and Dynamic KN Selection . Through various experiments, the authors demonstrate the existence of Knowledge Inconsistency (KII) and the limitations of the KL assumption . The experiments involve knowledge modification methods, such as Erasure and Update, to classify facts into Knowledge Inconsistency (KII) and Knowledge Inconsistency (KI) . The findings consistently show a high ratio of KII and low Consistency Scores (CS), indicating a significant difference between KI and KII . Additionally, the study conducts 39 sets of experiments across 13 experimental settings on 3 Pretrained Language Models (PLMs) to validate the conclusions rigorously . The proposed Consistency-Aware KN modification method outperforms baselines, providing further evidence for the validity of the Query Localization assumption .


What are the contributions of this paper?

The contributions of the paper "Knowledge Localization: Mission Not Accomplished? Enter Query Localization!" include:

  • Conducting an in-depth exploration of the Knowledge Localization assumption, showing that knowledge not conforming to this assumption is prevalent from statistical and knowledge modification perspectives .
  • Proposing a more realistic Query Localization assumption, which includes Query-KN Mapping and Dynamic KN Selection. Introducing a Consistency-Aware KN modification method that outperforms baselines in the "Erasure" setting on LLaMA3-8b .
  • Conducting 39 sets of experiments across 13 experimental settings on 3 PLMs, supplemented by additional visualization experiments to ensure the rigor of the conclusions .
  • Investigating the limitations of Knowledge Localization and demonstrating the existence of KII, which refers to knowledge that does not satisfy the knowledge localization assumption. The experiments used GPT-2, LLaMA2-7b, and LLaMA3-8b to assess the scalability of the methods and conclusions .

What work can be continued in depth?

To delve deeper into the research on large language models, there are several avenues for further exploration:

  • Understanding neural circuit function through synaptic engineering: This involves studying how neural circuits function through synaptic engineering, which can provide insights into the inner workings of language models .
  • Identifying semantic induction heads for in-context learning: Exploring semantic induction heads can enhance the understanding of how language models learn in context, contributing to advancements in model comprehension .
  • Investigating language-specific neurons for multilingual capabilities: Researching language-specific neurons can unlock the key to the multilingual capabilities of large language models, offering valuable insights into their linguistic processing mechanisms .
  • Measuring and improving consistency in pretrained language models: Further studies on measuring and enhancing consistency in pretrained language models can lead to advancements in model performance and reliability .
  • Dissecting recall of factual associations in auto-regressive language models: Delving into the recall of factual associations in auto-regressive language models can provide a deeper understanding of how these models retrieve and utilize information, contributing to improvements in their functionality .
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