activity
20232026
most citedPIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance

43 citations · 132 across the 11 of their papers we have counts for

collaborators

12 papers

cs.CL2026

Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale

Ang Li, Ben Liu, Bin Han +215

Efficient and scalable agentic intelligence requires models that can deliver both low-latency responses and strong reasoning capabilities while remaining practical to train, serve,…

cs.CL2025

MiraMind: Benchmarking Reliable Mental Health Reasoning beyond Answer Accuracy

Mengxi Xiao, Kailai Yang, Pengde Zhao +12

Mental-health reasoning with large language models (LLMs) is an evidence-constrained judgment problem: models must transform limited, subjective, and often ambiguous evidence into…

cs.CL2024★ 4 cited

Open-FinLLMs: Open Multimodal Large Language Models for Financial Applications

Jimin Huang, Mengxi Xiao, Dong Li +41

Financial LLMs hold promise for advancing financial tasks and domain-specific applications. However, they are limited by scarce corpora, weak multimodal capabilities, and narrow ev…

cs.HC2024★ 20 cited

HealMe: Harnessing Cognitive Reframing in Large Language Models for Psychotherapy

Mengxi Xiao, Qianqian Xie, Ziyan Kuang +5

Large Language Models (LLMs) can play a vital role in psychotherapy by adeptly handling the crucial task of cognitive reframing and overcoming challenges such as shame, distrust, t…

cs.CL2024★ 1 cited

Dólares or Dollars? Unraveling the Bilingual Prowess of Financial LLMs Between Spanish and English

Xiao Zhang, Ruoyu Xiang, Chenhan Yuan +8

Despite Spanish's pivotal role in the global finance industry, a pronounced gap exists in Spanish financial natural language processing (NLP) and application studies compared to En…

cs.CL2024★ 21 cited

FinBen: A Holistic Financial Benchmark for Large Language Models

Qianqian Xie, Weiguang Han, Zhengyu Chen +31

LLMs have transformed NLP and shown promise in various fields, yet their potential in finance is underexplored due to a lack of comprehensive evaluation benchmarks, the rapid devel…

cs.CL2023★ 5 cited

LAiW: A Chinese Legal Large Language Models Benchmark

Yongfu Dai, Duanyu Feng, Jimin Huang +6

General and legal domain LLMs have demonstrated strong performance in various tasks of LegalAI. However, the current evaluations of these LLMs in LegalAI are defined by the experts…

cs.LG2023★ 13 cited

Empowering Many, Biasing a Few: Generalist Credit Scoring through Large Language Models

Duanyu Feng, Yongfu Dai, Jimin Huang +6

In the financial industry, credit scoring is a fundamental element, shaping access to credit and determining the terms of loans for individuals and businesses alike. Traditional cr…

cs.CL2023★ 43 cited

PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance

Qianqian Xie, Weiguang Han, Xiao Zhang +4

Although large language models (LLMs) has shown great performance on natural language processing (NLP) in the financial domain, there are no publicly available financial tailtored…

cs.CL2023★ 24 cited

The Wall Street Neophyte: A Zero-Shot Analysis of ChatGPT Over MultiModal Stock Movement Prediction Challenges

Qianqian Xie, Weiguang Han, Yanzhao Lai +2

Recently, large language models (LLMs) like ChatGPT have demonstrated remarkable performance across a variety of natural language processing tasks. However, their effectiveness in…

q-fin.CP2023★ 1 cited

Mastering Pair Trading with Risk-Aware Recurrent Reinforcement Learning

Weiguang Han, Jimin Huang, Qianqian Xie +3

Although pair trading is the simplest hedging strategy for an investor to eliminate market risk, it is still a great challenge for reinforcement learning (RL) methods to perform pa…

q-fin.CP2023

Select and Trade: Towards Unified Pair Trading with Hierarchical Reinforcement Learning

Weiguang Han, Boyi Zhang, Qianqian Xie +3

Pair trading is one of the most effective statistical arbitrage strategies which seeks a neutral profit by hedging a pair of selected assets. Existing methods generally decompose t…