wawasan - Information Technology - # Entity Set Expansion (ESE)

UltraWiki: Ultra-fine-grained Entity Set Expansion with Negative Seed Entities

Q: How can the concept of negative seed entities be applied in other areas beyond ESE

The concept of negative seed entities can be applied in various other areas beyond Entity Set Expansion (ESE) to enhance the precision and specificity of models. Recommendation Systems: In recommendation systems, negative seed entities could be utilized to improve personalized recommendations by excluding items or products that a user has explicitly shown disinterest in. This helps in refining the recommendation process and providing more relevant suggestions. Search Engines: Negative seed entities can also play a crucial role in search engines by filtering out irrelevant results based on specific attributes or criteria specified by users. This ensures that search results are more tailored to individual preferences. Sentiment Analysis: In sentiment analysis, incorporating negative seed entities can help identify patterns or sentiments that are contrary to the norm, enabling a deeper understanding of nuanced opinions and emotions expressed in text data. Fraud Detection: Negative seed entities can aid in fraud detection algorithms by flagging behaviors or transactions that deviate from normal patterns, thereby improving the accuracy of detecting fraudulent activities. Healthcare: In healthcare applications, negative seed entities could assist in identifying symptoms or conditions that should not be present for accurate diagnosis and treatment planning. By leveraging negative seed entities across these diverse domains, models can better capture complex relationships and nuances within datasets, leading to more robust and effective decision-making processes.

Q: What potential limitations might arise from relying heavily on large language models like GPT-4

Relying heavily on large language models like GPT-4 comes with several potential limitations: Computational Resources: Large language models require significant computational resources for training and inference, making them inaccessible for many researchers or organizations with limited computing capabilities. Data Efficiency: Large language models often need massive amounts of data for training which may raise concerns about data privacy as well as environmental impact due to increased energy consumption during training. Bias Amplification: These models have been known to amplify biases present in the training data which can lead to unfair outcomes especially when used without proper mitigation strategies. Interpretability: The complexity of large language models makes it challenging to interpret their decisions and understand how they arrive at specific outputs, raising concerns about transparency and accountability. Domain Specificity: While large language models perform well on general tasks, they may struggle with domain-specific knowledge where specialized expertise is required.

Q: How could the introduction of external knowledge sources impact the performance of models like GenExpan

The introduction of external knowledge sources into models like GenExpan could have several impacts on performance: 1.Enhanced Semantic Understanding: External knowledge sources provide additional context and information that may not be available within the model's pretraining corpus, enhancing its semantic understanding capabilities. 2Improved Generalization: Accessing external knowledge allows the model to generalize better across different domains or topics by incorporating diverse perspectives from external sources. 3Reduced Bias: External knowledge sources can help mitigate bias present in pretraining data by introducing new viewpoints and counterbalancing existing biases within the model's internal representations 4Increased Robustness: By integrating information from external sources into entity generation processes like GenExpan ,the model becomes more robust against noise or inconsistencies present solely within its internal dataset 5Complexity Management: However,the integration must carefully manage complexities arising from varied quality levels,data consistency,and relevance ensuring only beneficial insights are incorporated while avoiding detrimental effects

Konsep Inti

The authors introduce negative seed entities to address the challenges of representing ultra-fine-grained semantic classes, leading to the creation of UltraWiki dataset for Ultra-ESE.

Abstrak

UltraWiki introduces negative seed entities to enhance ultra-fine-grained ESE, addressing ambiguity and defining "unwanted" semantics. The dataset encompasses 50,973 entities and 394,097 sentences across 236 ultra-fine-grained semantic classes. Two frameworks, RetExpan and GenExpan, are proposed for model evaluation.

The content discusses the challenges of traditional ESE methods in representing ultra-fine-grained semantic classes and introduces negative seed entities as a solution. The UltraWiki dataset is constructed to facilitate research in this area. Two frameworks, RetExpan and GenExpan, are proposed for evaluating large language models on the Ultra-ESE task.

Key points include:

Introduction of negative seed entities in ESE.
Creation of UltraWiki dataset tailored for Ultra-ESE.
Proposal of RetExpan and GenExpan frameworks for model evaluation.

Kustomisasi Ringkasan

Tulis Ulang dengan AI

Buat Sitasi

Terjemahkan Sumber

Ke Bahasa Lain

Buat Peta Pikiran

dari konten sumber

Kunjungi Sumber

arxiv.org

Statistik

UltraWiki encompasses 50,973 entities and 394,097 sentences.
The dataset includes 236 ultra-fine-grained semantic classes.
Each query is represented with 3-5 positive and negative seed entities.

Kutipan

"The challenge arises from substantial entity overlap among ultra-fine-grained semantic classes."
"Extensive experiments confirm the effectiveness of our proposed strategies."

Wawasan Utama Disaring Dari

UltraWiki

by Yangning Li,... pada arxiv.org 03-08-2024

https://arxiv.org/pdf/2403.04247.pdf

Pertanyaan yang Lebih Dalam

How can the concept of negative seed entities be applied in other areas beyond ESE

The concept of negative seed entities can be applied in various other areas beyond Entity Set Expansion (ESE) to enhance the precision and specificity of models.

Recommendation Systems: In recommendation systems, negative seed entities could be utilized to improve personalized recommendations by excluding items or products that a user has explicitly shown disinterest in. This helps in refining the recommendation process and providing more relevant suggestions.

Search Engines: Negative seed entities can also play a crucial role in search engines by filtering out irrelevant results based on specific attributes or criteria specified by users. This ensures that search results are more tailored to individual preferences.

Sentiment Analysis: In sentiment analysis, incorporating negative seed entities can help identify patterns or sentiments that are contrary to the norm, enabling a deeper understanding of nuanced opinions and emotions expressed in text data.

Fraud Detection: Negative seed entities can aid in fraud detection algorithms by flagging behaviors or transactions that deviate from normal patterns, thereby improving the accuracy of detecting fraudulent activities.

Healthcare: In healthcare applications, negative seed entities could assist in identifying symptoms or conditions that should not be present for accurate diagnosis and treatment planning.

By leveraging negative seed entities across these diverse domains, models can better capture complex relationships and nuances within datasets, leading to more robust and effective decision-making processes.

What potential limitations might arise from relying heavily on large language models like GPT-4

Relying heavily on large language models like GPT-4 comes with several potential limitations:

Computational Resources: Large language models require significant computational resources for training and inference, making them inaccessible for many researchers or organizations with limited computing capabilities.

Data Efficiency: Large language models often need massive amounts of data for training which may raise concerns about data privacy as well as environmental impact due to increased energy consumption during training.

Bias Amplification: These models have been known to amplify biases present in the training data which can lead to unfair outcomes especially when used without proper mitigation strategies.

Interpretability: The complexity of large language models makes it challenging to interpret their decisions and understand how they arrive at specific outputs, raising concerns about transparency and accountability.

Domain Specificity: While large language models perform well on general tasks, they may struggle with domain-specific knowledge where specialized expertise is required.

How could the introduction of external knowledge sources impact the performance of models like GenExpan

The introduction of external knowledge sources into models like GenExpan could have several impacts on performance:
1.Enhanced Semantic Understanding:

External knowledge sources provide additional context and information that may not be available within the model's pretraining corpus, enhancing its semantic understanding capabilities.
2Improved Generalization:

Accessing external knowledge allows the model to generalize better across different domains or topics by incorporating diverse perspectives from external sources.
3Reduced Bias:

External knowledge sources can help mitigate bias present in pretraining data by introducing new viewpoints and counterbalancing existing biases within the model's internal representations
4Increased Robustness:

By integrating information from external sources into entity generation processes like GenExpan ,the model becomes more robust against noise or inconsistencies present solely within its internal dataset
5Complexity Management:

However,the integration must carefully manage complexities arising from varied quality levels,data consistency,and relevance ensuring only beneficial insights are incorporated while avoiding detrimental effects