TrustDataFilter:Leveraging Trusted Knowledge Base Data for More Effective Filtering of Unknown Information
This work addresses data quality issues in domain-specific knowledge base construction, which is important for enterprises and researchers aiming to enhance model performance and innovation, though it appears incremental as it builds on existing NLI and LLM methods.
The paper tackles the problem of inaccurate and inconsistent data in domain-specific knowledge bases by proposing the Self-Natural Language Inference Data Filtering (self-nli-TDF) framework, which improves filtering performance by deducing reasoning relationships between trusted knowledge and data to be filtered, resulting in more consistent and reliable filtering outcomes as demonstrated in experiments across biology, radiation, and science domains.
With the advancement of technology and changes in the market, the demand for the construction of domain-specific knowledge bases has been increasing, either to improve model performance or to promote enterprise innovation and competitiveness. The construction of domain-specific knowledge bases typically relies on web crawlers or existing industry databases, leading to problems with accuracy and consistency of the data. To address these challenges, we considered the characteristics of domain data, where internal knowledge is interconnected, and proposed the Self-Natural Language Inference Data Filtering (self-nli-TDF) framework. This framework compares trusted filtered knowledge with the data to be filtered, deducing the reasoning relationship between them, thus improving filtering performance. The framework uses plug-and-play large language models for trustworthiness assessment and employs the RoBERTa-MNLI model from the NLI domain for reasoning. We constructed three datasets in the domains of biology, radiation, and science, and conducted experiments using RoBERTa, GPT3.5, and the local Qwen2 model. The experimental results show that this framework improves filter quality, producing more consistent and reliable filtering results.