74 citations · 176 across the 25 of their papers we have counts for
20 papers · 1 filter
NTDH: Complex Reasoning for Comprehensive Affective Analysis
Tianlei Zhu, Zhiwei Liu, Yuyan Wang +2
Comprehensive affective analysis is challenging for two reasons: it spans heterogeneous prediction tasks with continuous, ordinal, and multi-label outputs, and affective meaning is…
ShiJianBench: From Dialogue to Decision for Long-Horizon Evaluation of Investment Advisors
Jie Gong, Maowei Jiang, Zhiwei Liu +14
Conversational investment advisors influence not only what users know, but also how they make subsequent decisions as market conditions evolve. Existing evaluations primarily asses…
All That Glisters Is Not Gold: A Benchmark for Reference-Free Counterfactual Financial Misinformation Detection
Yuechen Jiang, Zhiwei Liu, Yupeng Cao +10
We introduce RFC Bench, a benchmark for evaluating large language models on financial misinformation under realistic news. RFC Bench operates at the paragraph level and captures th…
MisSpans: Fine-Grained False Span Identification in Cross-Domain Fake News
Zhiwei Liu, Paul Thompson, Jiaqi Rong +5
Online misinformation is increasingly pervasive, yet most existing benchmarks and methods evaluate veracity at the level of whole claims or paragraphs using coarse binary labels, o…
RAAR: Retrieval Augmented Agentic Reasoning for Cross-Domain Misinformation Detection
Zhiwei Liu, Runteng Guo, Baojie Qu +4
Cross-domain misinformation detection is challenging, as misinformation arises across domains with substantial differences in knowledge and discourse. Existing methods often rely o…
Same Claim, Different Judgment: Benchmarking Scenario-Induced Bias in Multilingual Financial Misinformation Detection
Zhiwei Liu, Yupen Cao, Yuechen Jiang +22
Large language models (LLMs) have been widely applied across various domains of finance. Since their training data are largely derived from human-authored corpora, LLMs may inherit…
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…
MMAFFBen: A Multilingual and Multimodal Affective Analysis Benchmark for Evaluating LLMs and VLMs
Zhiwei Liu, Lingfei Qian, Qianqian Xie +3
Large language models and vision-language models (which we jointly call LMs) have transformed NLP and CV, demonstrating remarkable potential across various fields. However, their c…
ConspEmoLLM-v2: A robust and stable model to detect sentiment-transformed conspiracy theories
Zhiwei Liu, Paul Thompson, Jiaqi Rong +1
Despite the many benefits of large language models (LLMs), they can also cause harm, e.g., through automatic generation of misinformation, including conspiracy theories. Moreover,…
Disentangled VAD Representations via a Variational Framework for Political Stance Detection
Beiyu Xu, Zhiwei Liu, Sophia Ananiadou
The stance detection task aims to categorise the stance regarding specified targets. Current methods face challenges in effectively integrating sentiment information for stance det…
Rumor Detection by Multi-task Suffix Learning based on Time-series Dual Sentiments
Zhiwei Liu, Kailai Yang, Eduard Hovy +1
The widespread dissemination of rumors on social media has a significant impact on people's lives, potentially leading to public panic and fear. Rumors often evoke specific sentime…
FMDLlama: Financial Misinformation Detection based on Large Language Models
Zhiwei Liu, Xin Zhang, Kailai Yang +3
The emergence of social media has made the spread of misinformation easier. In the financial domain, the accuracy of information is crucial for various aspects of financial market,…
Selective Preference Optimization via Token-Level Reward Function Estimation
Kailai Yang, Zhiwei Liu, Qianqian Xie +3
Recent advancements in large language model alignment leverage token-level supervisions to perform fine-grained preference optimization. However, existing token-level alignment met…
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…
RAEmoLLM: Retrieval Augmented LLMs for Cross-Domain Misinformation Detection Using In-Context Learning Based on Emotional Information
Zhiwei Liu, Kailai Yang, Qianqian Xie +3
Misinformation is prevalent in various fields such as education, politics, health, etc., causing significant harm to society. However, current methods for cross-domain misinformati…
MetaAligner: Towards Generalizable Multi-Objective Alignment of Language Models
Kailai Yang, Zhiwei Liu, Qianqian Xie +3
Recent advancements in large language models (LLMs) focus on aligning to heterogeneous human expectations and values via multi-objective preference alignment. However, existing met…
ConspEmoLLM: Conspiracy Theory Detection Using an Emotion-Based Large Language Model
Zhiwei Liu, Boyang Liu, Paul Thompson +2
The internet has brought both benefits and harms to society. A prime example of the latter is misinformation, including conspiracy theories, which flood the web. Recent advances in…
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…
EmoLLMs: A Series of Emotional Large Language Models and Annotation Tools for Comprehensive Affective Analysis
Zhiwei Liu, Kailai Yang, Tianlin Zhang +2
Sentiment analysis and emotion detection are important research topics in natural language processing (NLP) and benefit many downstream tasks. With the widespread application of LL…
Emotion Detection for Misinformation: A Review
Zhiwei Liu, Tianlin Zhang, Kailai Yang +3
With the advent of social media, an increasing number of netizens are sharing and reading posts and news online. However, the huge volumes of misinformation (e.g., fake news and ru…