Hao Chen

Hao Chen, Abdul Waheed, Xiang Li et al. · pku

The rise of Large Language Models (LLMs) has accentuated the need for diverse, high-quality pre-training data. Synthetic data emerges as a viable solution to the challenges of data scarcity and inaccessibility. While previous literature has focused predominantly on the quality and quantity of real data, our work enables the measurement of diversity in synthetic data and explores its impact on LLM performance. We study the downstream effects of synthetic data diversity during both the pre-training and fine-tuning stages by introducing a new diversity metric, \textit{LLM cluster-agent}, designed to evaluate the diversity of synthetic datasets. Through a series of controlled experiments with models of 350M and 1.4B parameters, we demonstrate that the proposed cluster-based LLM scoring of diversity correlates positively with both pre-training and supervised fine-tuning performance. Our findings also reveal that synthetic data diversity in pre-training affects supervised fine-tuning more significantly than pre-training itself, even for smaller models. We hope this study advances our understanding of the optimal use of synthetic data in LLM training and opens new avenues for efficient data generation processes.

Xiang Li, Che Wang, Bing Li et al.

In this paper, we propose a method for knowledge graph construction in power distribution networks. This method leverages entity features, which involve their semantic, phonetic, and syntactic characteristics, in both the knowledge graph of distribution network and the dispatching texts. An enhanced model based on Convolutional Neural Network, is utilized for effectively matching dispatch text entities with those in the knowledge graph. The effectiveness of this model is evaluated through experiments in real-world power distribution dispatch scenarios. The results indicate that, compared with the baselines, the proposed model excels in linking a variety of entity types, demonstrating high overall accuracy in power distribution knowledge graph construction task.

Chuqiao Song, Shunzhang Chen, Xinyi Cai et al.

Title: Sentiment Analysis of Spanish Political Party Communications on Twitter Using Pre-trained Language Models Authors: Chuqiao Song, Shunzhang Chen, Xinyi Cai, Hao Chen Comments: 21 pages, 6 figures Abstract: This study investigates sentiment patterns within Spanish political party communications on Twitter by leveraging BETO and RoBERTuito, two pre-trained language models optimized for Spanish text. Using a dataset of tweets from major Spanish political parties: PSOE, PP, Vox, Podemos, and Ciudadanos, spanning 2019 to 2024, this research analyzes sentiment distributions and explores the relationship between sentiment expression and party ideology. The findings indicate that both models consistently identify a predominant Neutral sentiment across all parties, with significant variations in Negative and Positive sentiments that align with ideological distinctions. Specifically, Vox exhibits higher levels of Negative sentiment, while PSOE demonstrates relatively high Positive sentiment, supporting the hypothesis that emotional appeals in political messaging reflect ideological stances. This study underscores the potential of pre-trained language models for non-English sentiment analysis on social media, providing insights into sentiment dynamics that shape public discourse within Spain's multi-party political system. Keywords: Spanish politics, sentiment analysis, pre-trained language models, Twitter, BETO, RoBERTuito, political ideology, multi-party system