Research Progress of Causal Inference in Bias Elimination
The natural language processing (NLP) models have recently gained widespread attention and are increasingly being applied to real-world tasks. However, due to the presence of bias, the application of NLP models in specialized fields has led to various issues. This paper introduces various types of b...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_02006.pdf |
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| Summary: | The natural language processing (NLP) models have recently gained widespread attention and are increasingly being applied to real-world tasks. However, due to the presence of bias, the application of NLP models in specialized fields has led to various issues. This paper introduces various types of biases and. through a comparative analysis of multiple existing methods, explains why previous approaches cannot fundamentally solve the bias problem. Additionally, this paper proposes that using causal inference to eliminate bias is an advanced method, and research teams that reduce bias by building causal relationship models have already studied this approach. This paper conducts a detailed analysis of several cutting-edge studies, exploring the practical application of causal inference in bias elimination and the challenges involved. Experimental results indicate that although causal inference methods have eliminated bias to some extent, further research and optimization are still needed. Finally, this paper summarizes the previous sections and provides an outlook on future research directions. |
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| ISSN: | 2271-2097 |