Papers by Jiageng Wu
Revealing COVID-19’s Social Dynamics: Diachronic Semantic Analysis of Vaccine and Symptom Discourse on Twitter (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Social media data provide a new source for social science and cultural analysis research, but its analysis is challenging due to the semantic shift phenomenon, where word meanings evolve over time. |
| Approach: | They propose an unsupervised dynamic word embedding method to capture longitudinal semantic shifts in social media data without predefined anchor words. |
| Outcome: | The proposed method captures longitudinal semantic shifts in social media data without predefined anchor words and leverages word co-occurrence statistics and dynamic updating to adapt embeddings over time. |
YATO: Yet Another deep learning based Text analysis Open toolkit (2023.emnlp-demo)
Copied to clipboard
| Challenge: | YATO is an open-source toolkit for text analysis with deep learning . it supports free combinations of three types of widely used features . |
| Approach: | They introduce YATO, an open-source toolkit for text analysis with deep learning. |
| Outcome: | YATO is an open-source toolkit for text analysis with deep learning . the toolkit supports free combinations of three types of widely used features . |
What Factors Affect LLMs and RLLMs in Financial Question Answering? (2026.findings-acl)
Copied to clipboard
| Challenge: | Recent studies have focused on large language models and reasoning large language model (RLLMs) however, there are few studies that explore what methods can fully unlock the performance of LLMs and RLLM in the financial domain. |
| Approach: | They examine the effects of prompting methods, agentic frameworks, and multilingual alignment methods on financial question-answering tasks. |
| Outcome: | The results show that prompting methods and agent frameworks improve LLMs' performance . the authors suggest that these frameworks can be used to enhance LLM performance if they are implemented in financial domains. |