Knowledge Circuits in Pretrained Transformers

Yunzhi Yao, Ningyu Zhang, Zekun Xi, Mengru Wang, Ziwen Xu, Shumin Deng, Huajun Chen·May 28, 2024

Summary

This paper investigates the knowledge storage and processing mechanisms in pretrained Transformers, focusing on GPT2 and TinyLLAMA, by introducing Knowledge Circuits. These circuits analyze the cooperation between attention heads, MLPs, and embeddings to understand how models encode and recall specific knowledge. The study reveals that knowledge is aggregated in earlier layers and enhanced in later ones, with special components like mover, relation, and mixture heads playing crucial roles. It evaluates the impact of editing techniques on these circuits, revealing their influence on factual recall and model behavior, such as hallucinations and in-context learning. The research employs ROME and FT-L0 editing methods, analyzing the role of edited layers and mover heads in information flow. It uses circuit theory to interpret model structure and behavior, identifying critical nodes and edges for performance. Experiments with GPT-2 Medium and TinyLLaMA show that knowledge circuits can maintain a significant portion of the model's ability even with reduced subgraphs, with improvements in specific areas. The study differentiates between models, finding that GPT-2 distributes knowledge across layers, while TinyLLAMA has a more concentrated pattern. It also highlights the importance of understanding layer dynamics, with attention and MLPs in lower layers for general information extraction and target entity positioning. The paper concludes by emphasizing the need for further research on improving knowledge editing methods for more reliable and safer language models, while addressing issues like overfitting and multi-hop factual knowledge challenges.

Key findings

4

Paper digest

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

The paper aims to address the issue of understanding the knowledge mechanisms of language models to enhance their design, editing, and overall performance in terms of knowledge representation, reasoning, factuality, and reducing hallucinations . This problem is not entirely new, as there are existing methods like circuit discovery-based patching, acdcpp, and Sparse Auto-Encoders that propose more efficient ways to analyze the information flow in models . The paper focuses on discovering circuits related to linguistic, factual, commonsense, and bias-related knowledge to ensure AI safety and privacy, indicating a novel approach to leveraging knowledge circuits for trustworthy AI .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis related to knowledge storage based on circuit theory and its effectiveness in analyzing knowledge mechanisms in language models . The study delves into manipulating language models to align with world knowledge or social value norms, focusing on knowledge editing, machine unlearning, and detoxification . The research explores the pivotal role of attention in knowledge representation and aims to manipulate specific knowledge in language models through knowledge circuits involving both MLP and attention components across different layers .


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

The paper "Knowledge Circuits in Pretrained Transformers" proposes several new ideas, methods, and models related to knowledge mechanisms in language models:

  • The paper introduces the Attention Lens method, which involves training a specific unembedding matrix to map each attention head into the vocabulary space, providing a potential starting point for understanding knowledge circuits within neural models .
  • It discusses the investigation of multi-hop factual shortcuts in knowledge editing of large language models, aiming to enhance factuality and alleviate hallucinations .
  • The research delves into the discovery of knowledge-critical subnetworks in pretrained language models, focusing on linguistic, factual, commonsense, and bias-related knowledge .
  • The paper also explores the concept of knowledge unlearning for large language models, addressing tasks, methods, and challenges associated with unlearning knowledge .
  • Additionally, it presents a method for model deficiency unlearning via parameter-efficient module operation, aiming to separate valuable information from irrelevant data in language models .

These proposed ideas, methods, and models contribute to a deeper understanding of knowledge mechanisms in language models, offering insights for designing and editing language models, improving reasoning, enhancing factuality, and ensuring trustworthy AI applications . The paper "Knowledge Circuits in Pretrained Transformers" introduces novel characteristics and advantages compared to previous methods in the realm of knowledge mechanisms in language models:

  • The research focuses on knowledge circuits, elucidating internal mechanisms for knowledge editing and evaluating the impact of previous knowledge editing methods like ROME and FT-M, emphasizing the importance of the layer hyper-parameter in knowledge editing and the effectiveness of different editing layers .
  • It delves into the discovery of knowledge-critical subnetworks in pretrained language models, particularly focusing on linguistic, factual, commonsense, and bias-related knowledge, which contributes to a deeper understanding of knowledge mechanisms within neural models .
  • The paper discusses the Attention Lens method, which involves training a specific unembedding matrix to map each attention head into the vocabulary space, offering a potential starting point for understanding knowledge circuits within neural models and shedding light on the activation mechanisms of attention heads .
  • Furthermore, the research explores the concept of knowledge unlearning for large language models, addressing tasks, methods, and challenges associated with unlearning knowledge, which can lead to model deficiency unlearning via parameter-efficient module operation, separating valuable information from irrelevant data in language models .
  • By focusing on linguistic, factual, commonsense, and bias-related knowledge, the proposed approach in the paper can be applied to ensure safety and privacy information, promoting trustworthy AI applications and enhancing the factuality and reasoning capabilities of language models .

These characteristics and advancements highlight the paper's contributions to the field of knowledge mechanisms in language models, offering insights into knowledge editing, circuit discovery, and model refinement for improved performance and reliability in AI applications.


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 research works exist in the field of knowledge circuits in pretrained transformers. Noteworthy researchers in this area include Cunxiang Wang, Xiaoze Liu, Yuanhao Yue, Xiangru Tang, Tianhang Zhang, Cheng Jiayang, Yunzhi Yao, Wenyang Gao, Xuming Hu, Zehan Qi, Yidong Wang, Linyi Yang, Jindong Wang, Xing Xie, Zheng Zhang, Yue Zhang, and many others . These researchers have contributed to various aspects of understanding knowledge mechanisms in language models.

The key to the solution mentioned in the paper involves a new perspective on knowledge storage based on circuit theory. The paper conducts a preliminary analysis to demonstrate the effectiveness of this approach in enhancing the design and editing of language models, improving knowledge, reasoning, factuality, and mitigating hallucinations . By focusing on linguistic, factual, commonsense, and bias-related knowledge, this approach aims to ensure safety, privacy, and promote trustworthy AI .


How were the experiments in the paper designed?

The experiments in the paper were designed to explore the impact of knowledge editing methods on language models' original knowledge representations and behaviors. The goal was to elucidate the internal mechanisms for knowledge editing and interpret complex behaviors of language models across various domains, including factual, bias, linguistic, and commonsense knowledge . The experiments involved constructing knowledge circuits associated with different expressions of knowledge stored in the language model. These circuits were used to analyze the information flow within specific pieces of knowledge and understand how the model aggregates knowledge in the earlier to middle layers and enhances it in the later layers . The experiments also focused on evaluating the performance of different editing layers in knowledge editing methods like ROME and FT-M, comparing the knowledge circuits computed by the edited model with the original one to assess their effectiveness . Additionally, the experiments involved manipulating the model's computation by targeting critical points within the circuit, such as masking edges to make the model less toxic and safer, demonstrating the effectiveness of the circuits in influencing the model's behavior .


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

The dataset used for quantitative evaluation in the study is not explicitly mentioned in the provided contexts. However, the code for the research work is open source and can be accessed through the provided URLs in the citations .


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 substantial support for the scientific hypotheses that require verification. The study delves into the knowledge storage in language models, specifically focusing on the role of attention heads and MLPs in representing knowledge . The findings reveal that certain attention heads, such as Mover Heads, Relation Heads, and Mix Heads, play crucial roles in the final predictions of the model, aligning with previous research . These specialized components within the model contribute significantly to the behavior and performance of the language model, supporting the hypothesis that attention mechanisms and MLPs are pivotal in knowledge representation .

Moreover, the paper discusses the manipulation of language models to align them with world knowledge or social value norms through knowledge editing and other techniques . By modifying the MLPs in the language models based on specific factual knowledge, researchers aim to change the model's behavior, demonstrating the importance of attention mechanisms in knowledge representation . This manipulation of specific knowledge via knowledge circuits across different layers further supports the hypothesis that attention components and MLPs are essential for shaping the behavior of language models based on factual knowledge .

Overall, the experiments and results outlined in the paper provide strong empirical evidence and analysis to support the scientific hypotheses related to knowledge storage, manipulation, and representation in pretrained transformers. The findings shed light on the intricate mechanisms within language models, emphasizing the critical role of attention heads and MLPs in encoding and processing factual knowledge, thereby validating the scientific hypotheses under investigation .


What are the contributions of this paper?

The paper makes several contributions, including:

  • Surveying factuality in large language models, focusing on knowledge, retrieval, and domain-specificity .
  • Discussing unified hallucination detection for multimodal large language models .
  • Exploring trustworthiness in large language models through the TrustLLM framework .
  • Evaluating the safety of large language models with multiple choice questions using Safetybench .
  • Investigating multi-hop factual shortcuts in knowledge editing of large language models .
  • Introducing a framework for inspecting hidden representations of language models called Patchscopes .
  • Providing insights into model deficiency unlearning via parameter-efficient module operation .

What work can be continued in depth?

Further research in the field of Transformers and language models can be expanded in several directions:

  • Exploring knowledge circuits: Delving deeper into the computation graph of language models to uncover knowledge circuits that play a crucial role in articulating specific knowledge .
  • Circuit discovery: Conducting more studies to identify circuits within language models by systematically altering the model's edges and nodes to observe their effects on performance, which can provide insights into the functioning and constraints of these models .
  • Improving knowledge circuit discovery: There is a significant room for improvement in knowledge circuit discovery methods, such as proposing more efficient ways to build the model's information flow and discovering circuits using alternative methods like acdcpp and Sparse Auto-Encoders .
  • Enhancing model understanding: Focusing on linguistic, factual, commonsense, and bias-related knowledge to ensure safety, privacy, and trustworthy AI, which can contribute to better designing and editing language models for improved reasoning and factuality .

Tables

3

Introduction
Background
Evolution of Transformer models and their role in NLP
GPT2 and TinyLLAMA as prominent models
Objective
To explore knowledge mechanisms in Transformers
Analyze cooperation between components in GPT2 and TinyLLAMA
Evaluate editing techniques and their impact on factual recall
Method
Data Collection and Analysis
Model Inspection
GPT2 and TinyLLAMA architecture overview
Knowledge Circuits: A New Approach
Definition and creation of knowledge circuits
Editing Techniques: ROME and FT-L0
Application and evaluation of editing methods
Data Preprocessing and Circuit Interpretation
Layer Analysis
Aggregation and enhancement of knowledge across layers
Mover, Relation, and Mixture Heads
Identification of critical components for knowledge processing
Experimentation
GPT-2 Medium and TinyLLAMA Evaluation
Subgraph reduction and circuit performance
Factual recall and model behavior analysis
Results and Findings
Model Differentiation
GPT-2's distributed knowledge vs. TinyLLAMA's concentration
Layer dynamics and their roles in information processing
Editing Techniques' Impact
Factual recall improvements and model behavior changes
Hallucinations and in-context learning implications
Discussion
Layer Dynamics and Information Flow
Attention and MLPs in lower layers
Target entity positioning and general knowledge extraction
Limitations and Future Research
Overfitting, multi-hop factual knowledge, and safety concerns
Opportunities for improved knowledge editing methods
Conclusion
The significance of understanding knowledge circuits in Transformers
Call for further research on enhancing language models
Addressing challenges in reliability and safety
Basic info
papers
computation and language
computer vision and pattern recognition
information retrieval
machine learning
artificial intelligence
Advanced features
Insights
Which models, GPT2 and TinyLLAMA, are the focus of the investigation?
What are Knowledge Circuits used for in the paper?
What insights are gained about knowledge distribution and layer dynamics in GPT-2 and TinyLLAMA?
How do the study's editing techniques impact factual recall and model behavior?

Knowledge Circuits in Pretrained Transformers

Yunzhi Yao, Ningyu Zhang, Zekun Xi, Mengru Wang, Ziwen Xu, Shumin Deng, Huajun Chen·May 28, 2024

Summary

This paper investigates the knowledge storage and processing mechanisms in pretrained Transformers, focusing on GPT2 and TinyLLAMA, by introducing Knowledge Circuits. These circuits analyze the cooperation between attention heads, MLPs, and embeddings to understand how models encode and recall specific knowledge. The study reveals that knowledge is aggregated in earlier layers and enhanced in later ones, with special components like mover, relation, and mixture heads playing crucial roles. It evaluates the impact of editing techniques on these circuits, revealing their influence on factual recall and model behavior, such as hallucinations and in-context learning. The research employs ROME and FT-L0 editing methods, analyzing the role of edited layers and mover heads in information flow. It uses circuit theory to interpret model structure and behavior, identifying critical nodes and edges for performance. Experiments with GPT-2 Medium and TinyLLaMA show that knowledge circuits can maintain a significant portion of the model's ability even with reduced subgraphs, with improvements in specific areas. The study differentiates between models, finding that GPT-2 distributes knowledge across layers, while TinyLLAMA has a more concentrated pattern. It also highlights the importance of understanding layer dynamics, with attention and MLPs in lower layers for general information extraction and target entity positioning. The paper concludes by emphasizing the need for further research on improving knowledge editing methods for more reliable and safer language models, while addressing issues like overfitting and multi-hop factual knowledge challenges.
Mind map
Factual recall and model behavior analysis
Subgraph reduction and circuit performance
Identification of critical components for knowledge processing
Aggregation and enhancement of knowledge across layers
Application and evaluation of editing methods
Definition and creation of knowledge circuits
GPT2 and TinyLLAMA architecture overview
Opportunities for improved knowledge editing methods
Overfitting, multi-hop factual knowledge, and safety concerns
Target entity positioning and general knowledge extraction
Attention and MLPs in lower layers
Hallucinations and in-context learning implications
Factual recall improvements and model behavior changes
Layer dynamics and their roles in information processing
GPT-2's distributed knowledge vs. TinyLLAMA's concentration
GPT-2 Medium and TinyLLAMA Evaluation
Mover, Relation, and Mixture Heads
Layer Analysis
Editing Techniques: ROME and FT-L0
Knowledge Circuits: A New Approach
Model Inspection
Evaluate editing techniques and their impact on factual recall
Analyze cooperation between components in GPT2 and TinyLLAMA
To explore knowledge mechanisms in Transformers
GPT2 and TinyLLAMA as prominent models
Evolution of Transformer models and their role in NLP
Addressing challenges in reliability and safety
Call for further research on enhancing language models
The significance of understanding knowledge circuits in Transformers
Limitations and Future Research
Layer Dynamics and Information Flow
Editing Techniques' Impact
Model Differentiation
Experimentation
Data Preprocessing and Circuit Interpretation
Data Collection and Analysis
Objective
Background
Conclusion
Discussion
Results and Findings
Method
Introduction
Outline
Introduction
Background
Evolution of Transformer models and their role in NLP
GPT2 and TinyLLAMA as prominent models
Objective
To explore knowledge mechanisms in Transformers
Analyze cooperation between components in GPT2 and TinyLLAMA
Evaluate editing techniques and their impact on factual recall
Method
Data Collection and Analysis
Model Inspection
GPT2 and TinyLLAMA architecture overview
Knowledge Circuits: A New Approach
Definition and creation of knowledge circuits
Editing Techniques: ROME and FT-L0
Application and evaluation of editing methods
Data Preprocessing and Circuit Interpretation
Layer Analysis
Aggregation and enhancement of knowledge across layers
Mover, Relation, and Mixture Heads
Identification of critical components for knowledge processing
Experimentation
GPT-2 Medium and TinyLLAMA Evaluation
Subgraph reduction and circuit performance
Factual recall and model behavior analysis
Results and Findings
Model Differentiation
GPT-2's distributed knowledge vs. TinyLLAMA's concentration
Layer dynamics and their roles in information processing
Editing Techniques' Impact
Factual recall improvements and model behavior changes
Hallucinations and in-context learning implications
Discussion
Layer Dynamics and Information Flow
Attention and MLPs in lower layers
Target entity positioning and general knowledge extraction
Limitations and Future Research
Overfitting, multi-hop factual knowledge, and safety concerns
Opportunities for improved knowledge editing methods
Conclusion
The significance of understanding knowledge circuits in Transformers
Call for further research on enhancing language models
Addressing challenges in reliability and safety
Key findings
4

Paper digest

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

The paper aims to address the issue of understanding the knowledge mechanisms of language models to enhance their design, editing, and overall performance in terms of knowledge representation, reasoning, factuality, and reducing hallucinations . This problem is not entirely new, as there are existing methods like circuit discovery-based patching, acdcpp, and Sparse Auto-Encoders that propose more efficient ways to analyze the information flow in models . The paper focuses on discovering circuits related to linguistic, factual, commonsense, and bias-related knowledge to ensure AI safety and privacy, indicating a novel approach to leveraging knowledge circuits for trustworthy AI .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis related to knowledge storage based on circuit theory and its effectiveness in analyzing knowledge mechanisms in language models . The study delves into manipulating language models to align with world knowledge or social value norms, focusing on knowledge editing, machine unlearning, and detoxification . The research explores the pivotal role of attention in knowledge representation and aims to manipulate specific knowledge in language models through knowledge circuits involving both MLP and attention components across different layers .


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

The paper "Knowledge Circuits in Pretrained Transformers" proposes several new ideas, methods, and models related to knowledge mechanisms in language models:

  • The paper introduces the Attention Lens method, which involves training a specific unembedding matrix to map each attention head into the vocabulary space, providing a potential starting point for understanding knowledge circuits within neural models .
  • It discusses the investigation of multi-hop factual shortcuts in knowledge editing of large language models, aiming to enhance factuality and alleviate hallucinations .
  • The research delves into the discovery of knowledge-critical subnetworks in pretrained language models, focusing on linguistic, factual, commonsense, and bias-related knowledge .
  • The paper also explores the concept of knowledge unlearning for large language models, addressing tasks, methods, and challenges associated with unlearning knowledge .
  • Additionally, it presents a method for model deficiency unlearning via parameter-efficient module operation, aiming to separate valuable information from irrelevant data in language models .

These proposed ideas, methods, and models contribute to a deeper understanding of knowledge mechanisms in language models, offering insights for designing and editing language models, improving reasoning, enhancing factuality, and ensuring trustworthy AI applications . The paper "Knowledge Circuits in Pretrained Transformers" introduces novel characteristics and advantages compared to previous methods in the realm of knowledge mechanisms in language models:

  • The research focuses on knowledge circuits, elucidating internal mechanisms for knowledge editing and evaluating the impact of previous knowledge editing methods like ROME and FT-M, emphasizing the importance of the layer hyper-parameter in knowledge editing and the effectiveness of different editing layers .
  • It delves into the discovery of knowledge-critical subnetworks in pretrained language models, particularly focusing on linguistic, factual, commonsense, and bias-related knowledge, which contributes to a deeper understanding of knowledge mechanisms within neural models .
  • The paper discusses the Attention Lens method, which involves training a specific unembedding matrix to map each attention head into the vocabulary space, offering a potential starting point for understanding knowledge circuits within neural models and shedding light on the activation mechanisms of attention heads .
  • Furthermore, the research explores the concept of knowledge unlearning for large language models, addressing tasks, methods, and challenges associated with unlearning knowledge, which can lead to model deficiency unlearning via parameter-efficient module operation, separating valuable information from irrelevant data in language models .
  • By focusing on linguistic, factual, commonsense, and bias-related knowledge, the proposed approach in the paper can be applied to ensure safety and privacy information, promoting trustworthy AI applications and enhancing the factuality and reasoning capabilities of language models .

These characteristics and advancements highlight the paper's contributions to the field of knowledge mechanisms in language models, offering insights into knowledge editing, circuit discovery, and model refinement for improved performance and reliability in AI applications.


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 research works exist in the field of knowledge circuits in pretrained transformers. Noteworthy researchers in this area include Cunxiang Wang, Xiaoze Liu, Yuanhao Yue, Xiangru Tang, Tianhang Zhang, Cheng Jiayang, Yunzhi Yao, Wenyang Gao, Xuming Hu, Zehan Qi, Yidong Wang, Linyi Yang, Jindong Wang, Xing Xie, Zheng Zhang, Yue Zhang, and many others . These researchers have contributed to various aspects of understanding knowledge mechanisms in language models.

The key to the solution mentioned in the paper involves a new perspective on knowledge storage based on circuit theory. The paper conducts a preliminary analysis to demonstrate the effectiveness of this approach in enhancing the design and editing of language models, improving knowledge, reasoning, factuality, and mitigating hallucinations . By focusing on linguistic, factual, commonsense, and bias-related knowledge, this approach aims to ensure safety, privacy, and promote trustworthy AI .


How were the experiments in the paper designed?

The experiments in the paper were designed to explore the impact of knowledge editing methods on language models' original knowledge representations and behaviors. The goal was to elucidate the internal mechanisms for knowledge editing and interpret complex behaviors of language models across various domains, including factual, bias, linguistic, and commonsense knowledge . The experiments involved constructing knowledge circuits associated with different expressions of knowledge stored in the language model. These circuits were used to analyze the information flow within specific pieces of knowledge and understand how the model aggregates knowledge in the earlier to middle layers and enhances it in the later layers . The experiments also focused on evaluating the performance of different editing layers in knowledge editing methods like ROME and FT-M, comparing the knowledge circuits computed by the edited model with the original one to assess their effectiveness . Additionally, the experiments involved manipulating the model's computation by targeting critical points within the circuit, such as masking edges to make the model less toxic and safer, demonstrating the effectiveness of the circuits in influencing the model's behavior .


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

The dataset used for quantitative evaluation in the study is not explicitly mentioned in the provided contexts. However, the code for the research work is open source and can be accessed through the provided URLs in the citations .


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 substantial support for the scientific hypotheses that require verification. The study delves into the knowledge storage in language models, specifically focusing on the role of attention heads and MLPs in representing knowledge . The findings reveal that certain attention heads, such as Mover Heads, Relation Heads, and Mix Heads, play crucial roles in the final predictions of the model, aligning with previous research . These specialized components within the model contribute significantly to the behavior and performance of the language model, supporting the hypothesis that attention mechanisms and MLPs are pivotal in knowledge representation .

Moreover, the paper discusses the manipulation of language models to align them with world knowledge or social value norms through knowledge editing and other techniques . By modifying the MLPs in the language models based on specific factual knowledge, researchers aim to change the model's behavior, demonstrating the importance of attention mechanisms in knowledge representation . This manipulation of specific knowledge via knowledge circuits across different layers further supports the hypothesis that attention components and MLPs are essential for shaping the behavior of language models based on factual knowledge .

Overall, the experiments and results outlined in the paper provide strong empirical evidence and analysis to support the scientific hypotheses related to knowledge storage, manipulation, and representation in pretrained transformers. The findings shed light on the intricate mechanisms within language models, emphasizing the critical role of attention heads and MLPs in encoding and processing factual knowledge, thereby validating the scientific hypotheses under investigation .


What are the contributions of this paper?

The paper makes several contributions, including:

  • Surveying factuality in large language models, focusing on knowledge, retrieval, and domain-specificity .
  • Discussing unified hallucination detection for multimodal large language models .
  • Exploring trustworthiness in large language models through the TrustLLM framework .
  • Evaluating the safety of large language models with multiple choice questions using Safetybench .
  • Investigating multi-hop factual shortcuts in knowledge editing of large language models .
  • Introducing a framework for inspecting hidden representations of language models called Patchscopes .
  • Providing insights into model deficiency unlearning via parameter-efficient module operation .

What work can be continued in depth?

Further research in the field of Transformers and language models can be expanded in several directions:

  • Exploring knowledge circuits: Delving deeper into the computation graph of language models to uncover knowledge circuits that play a crucial role in articulating specific knowledge .
  • Circuit discovery: Conducting more studies to identify circuits within language models by systematically altering the model's edges and nodes to observe their effects on performance, which can provide insights into the functioning and constraints of these models .
  • Improving knowledge circuit discovery: There is a significant room for improvement in knowledge circuit discovery methods, such as proposing more efficient ways to build the model's information flow and discovering circuits using alternative methods like acdcpp and Sparse Auto-Encoders .
  • Enhancing model understanding: Focusing on linguistic, factual, commonsense, and bias-related knowledge to ensure safety, privacy, and trustworthy AI, which can contribute to better designing and editing language models for improved reasoning and factuality .
Tables
3
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