Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data.

Pingping Chen 0004

dblp:88/10173-4 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
1since 2021 · last 2025
0009-0008-6979-401XORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Information retrieval · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information retrieval › document retrieval › domain-specific retrieval
financial information retrieval
0.912025
FinIR: The 2nd Workshop on Financial Information Retrieval in the Era of Generative AI · SIGIR 2025
Information retrieval
retrieval-augmented generation
0.912025
FinIR: The 2nd Workshop on Financial Information Retrieval in the Era of Generative AI · SIGIR 2025
Information retrieval › evaluation
benchmark
0.312025
FinIR: The 2nd Workshop on Financial Information Retrieval in the Era of Generative AI · SIGIR 2025
Information retrieval
evaluation
0.312025
FinIR: The 2nd Workshop on Financial Information Retrieval in the Era of Generative AI · SIGIR 2025
Information retrieval
trustworthy information retrieval
0.312025
FinIR: The 2nd Workshop on Financial Information Retrieval in the Era of Generative AI · SIGIR 2025

Methods — techniques the papers use, named apart from their topics

large language model · 0.9
YearPublicationVenuePosition
2025 FinIR: The 2nd Workshop on Financial Information Retrieval in the Era of Generative AI
abstract
Recent advancements in Generative AI, such as Large Language Models (LLMs), have demonstrated remarkable success across various general tasks. Extensive studies have explored leveraging generative models in finance, but significant challenges persist. This half-day workshop explores potential approaches and research directions to address these challenges by equipping generative models with advanced Information Retrieval (IR) models. Specifically, this workshop seeks to provide a platform for discussing innovative ideas that facilitate the advancement of IR technology to enrich generative models in finance from four key perspectives: (i) financial IR techniques (ii) financial IR benchmarking and evaluation (iii) financial systems and agents/assistants (iv) and trustworthiness, privacy and security when applying financial IR and generative models. This workshop aims to deepen understanding, accelerate progress, and support the advancement of IR technology to enhance generative models to address financial challenges.
Fengbin Zhu, Yunshan Ma 0002, Fuli Feng, Chao Wang 0049, Huan-Bo Luan, Guangnan Ye, Shuo Zhang 0006, Dhagash Mehta, Pingping Chen 0004, Bing Xiang, Tat-Seng Chua
SIGIR9