Zobrazeno 1 - 10
of 211
pro vyhledávání: '"Takamura, Hiroya"'
Professionals' decisions are the focus of every field. For example, politicians' decisions will influence the future of the country, and stock analysts' decisions will impact the market. Recognizing the influential role of professionals' perspectives
Externí odkaz:
http://arxiv.org/abs/2410.07225
The advancement of text generation models has granted us the capability to produce coherent and convincing text on demand. Yet, in real-life circumstances, individuals do not continuously generate text or voice their opinions. For instance, consumers
Externí odkaz:
http://arxiv.org/abs/2410.01169
This paper explores designing artificial organizations with professional behavior in investments using a multi-agent simulation. The method mimics hierarchical decision-making in investment firms, using news articles to inform decisions. A large-scal
Externí odkaz:
http://arxiv.org/abs/2410.00354
Question answering (QA) has been a long-standing focus in the NLP field, predominantly addressing reading comprehension and common sense QA. However, scenarios involving the preparation of answers to probable questions during professional oral presen
Externí odkaz:
http://arxiv.org/abs/2409.18678
This paper investigates the role of expert-designed hint in enhancing sentiment analysis on financial social media posts. We explore the capability of large language models (LLMs) to empathize with writer perspectives and analyze sentiments. Our find
Externí odkaz:
http://arxiv.org/abs/2409.17448
Autor:
Chen, Chung-Chi, Huang, Hen-Hsen, Chen, Hsin-Hsi, Takamura, Hiroya, Kobayashi, Ichiro, Miyao, Yusuke
In the era of rapid Internet and social media platform development, individuals readily share their viewpoints online. The overwhelming quantity of these posts renders comprehensive analysis impractical. This necessitates an efficient recommendation
Externí odkaz:
http://arxiv.org/abs/2409.17417
In the post-Turing era, evaluating large language models (LLMs) involves assessing generated text based on readers' reactions rather than merely its indistinguishability from human-produced content. This paper explores how LLM-generated text impacts
Externí odkaz:
http://arxiv.org/abs/2409.16710
Thinking about the future is one of the important activities that people do in daily life. Futurists also pay a lot of effort into figuring out possible scenarios for the future. We argue that the exploration of this direction is still in an early st
Externí odkaz:
http://arxiv.org/abs/2405.20708
Large language models (LLMs) have been applied to a wide range of data-to-text generation tasks, including tables, graphs, and time-series numerical data-to-text settings. While research on generating prompts for structured data such as tables and gr
Externí odkaz:
http://arxiv.org/abs/2404.02466
In this paper, we propose methods for discovering semantic differences in words appearing in two corpora based on the norms of contextualized word vectors. The key idea is that the coverage of meanings is reflected in the norm of its mean word vector
Externí odkaz:
http://arxiv.org/abs/2305.11516