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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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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| author | Chen Limanxi |
| author_facet | Chen Limanxi |
| author_sort | Chen Limanxi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9df06bf3ddfa4c5e9d45b6be0079d342 |
| institution | Kabale University |
| issn | 2271-2097 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-9df06bf3ddfa4c5e9d45b6be0079d3422025-02-07T08:21:10ZengEDP SciencesITM Web of Conferences2271-20972025-01-01700200610.1051/itmconf/20257002006itmconf_dai2024_02006Research Progress of Causal Inference in Bias EliminationChen Limanxi0College of Computer and Big Data, Fuzhou UniversityThe 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.https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_02006.pdf |
| spellingShingle | Chen Limanxi Research Progress of Causal Inference in Bias Elimination ITM Web of Conferences |
| title | Research Progress of Causal Inference in Bias Elimination |
| title_full | Research Progress of Causal Inference in Bias Elimination |
| title_fullStr | Research Progress of Causal Inference in Bias Elimination |
| title_full_unstemmed | Research Progress of Causal Inference in Bias Elimination |
| title_short | Research Progress of Causal Inference in Bias Elimination |
| title_sort | research progress of causal inference in bias elimination |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_02006.pdf |
| work_keys_str_mv | AT chenlimanxi researchprogressofcausalinferenceinbiaselimination |