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Efficient Large Language Model Based Passage Re-Ranking Using Single Token Representations
Jeongwoo Na, Jun Kwon, Eunseong Choi, Jongwuk Lee
http://doi.org/10.5626/JOK.2025.52.5.395
In information retrieval systems, document re-ranking involves reordering a set of candidate documents based on evaluation of their relevance to a given query. Leveraging extensive natural language understanding capabilities of large language models(LLMs), numerous studies on document re-ranking have been conducted, demonstrating groundbreaking performance. However, studies utilizing large language models focus solely on improving reranking performance, resulting in degraded efficiency due to excessively long input sequences and the need for repetitive inference. To address these limitations, we propose ListT5++, a novel model that represents the relevance between a query and a passage using single token embedding and significantly improves the efficiency of LLM-based reranking through a single-step decoding strategy that minimizes the decoding process. Experimental results showed that ListT5++ could maintain accuracy levels comparable to existing methods while reducing inference latency by a factor of 29.4 relative to the baseline. Moreover, our approach demonstrates robust characteristics by being insensitive to th initial ordering of candidate documents, thereby ensuring high practicality in real-time retrieval environments.
Multidimensional Subset-based Systems for Bias Elimination Within Binary Classification Datasets
KyeongSu Byun, Goo Kim, Joonho Kwon
http://doi.org/10.5626/JOK.2023.50.5.383
As artificial intelligence technology develops, artificial intelligence-related fairness issues are drawing attention. As a result, many related studies have been conducted on this issue, but most of the research has focused on developing models and training methods. Research on removing bias existing in data used for learning, which is a fundamental cause, is still insufficient. Therefore, in this paper, we designed and implemented a system that divides the biases existing within the data into label biases and subgroup biases and removes the biases to generate datasets with improved fairness. The proposed system consists of two steps: (1) subset generation and (2) bias removal. First, the subset generator divides the existing data into subsets on formed by a combination of values in an datasets. Subsequently, the subset is divided into dominant and weak groups based on the fairness indicator values obtained by validating the existing datasets based on the validation datasets. Next, the bias remover reduces the bias shown in the subset by repeating the process of sequentially extracting and verifying the dominant group of each subset to reduce the difference from the weak group. Afterwards, the biased subsets are merged and a fair data set is returned. The fairness indicators used for the verification use the F1 score and the equalized odd. Comprehensive experiments with real-world Census incoming data, COMPAS data, and bank marketing data as verification data demonstrated that our proposed system outperformed the existing technique by yielding a better fairness improvement rate and providing more accuracy in most machine learning algorithms.
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