文献类型：期刊文献

英文题名：Context-Aware Vectors: A New Method Integrating Personality Into LLMs for?Enhanced Sentiment Analysis

作者：Shuai, Zhihao[1]; Li, Kaiwen[2]; Li, Guoyu[3]; Liu, Shengyao[4]; Li, Dandan[1]; Tang, Naisheng[3]

机构：[1] The Hong Kong University of Science and Technology [Guangzhou], Guangzhou, China; [2] South China University of Technology, Guangzhou, China; [3] University of Electronic Science and Technology of China, Chengdu, China; [4] Guangdong Ocean University, Zhanjiang, China

年份：2026

卷号：15923 LNCS

起止页码：59

外文期刊名：Lecture Notes in Computer Science

收录：EI(收录号：20254819619067)

语种：英文

外文关键词：Data mining - Vectors

外文摘要：This research is dedicated to surmounting the limitations of Large Language Models (LLMs) in sentiment analysis. By delving into Myers-Briggs Type Indicator (MBTI) traits, we put forward an innovative Context-Aware Vectors method. Departing from traditional approaches such as rule-setting or data fine-tuning, we explore the latent interpretability of personality traits within LLMs. We conduct experiments on Meta-LLaMA-3.1-8B, employing the SCIFACT dataset devoid of personality bias and carefully crafted prompts. This enables us to extract context-aware vectors, by calculating the differences in the outputs of a specific layer under different MBTI Traits styles. When evaluated on the SemEval dataset, LLMs integrated with context-aware vectors, especially the INFJ-LLM, achieve state-of-the-art (SOTA) performance in comparison to 15 baseline models. Ablation experiments further verify the significance of each component of our context-aware vectors. In summary, this work offers a novel approach to integrating MBTI traits into LLMs, expands the scope of LLM interpretability research, and notably enhances LLMs’ performance in sentiment analysis. ? The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.