1 citations · 1 across the 9 of their papers we have counts for
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LiveFact: A Dynamic, Time-Aware Benchmark for LLM-Driven Fake News Detection
Cheng Xu, Changhong Jin, Yingjie Niu +5
The rapid development of Large Language Models (LLMs) has transformed fake news detection and fact-checking tasks from simple classification to complex reasoning. However, evaluati…
Relational Probing: LM-to-Graph Adaptation for Financial Prediction
Yingjie Niu, Changhong Jin, Rian Dolphin +1
Language models can be used to identify relationships between financial entities in text. However, while structured output mechanisms exist, prompting-based pipelines still incur a…
When Silence Is Golden: Can LLMs Learn to Abstain in Temporal QA and Beyond?
Xinyu Zhou, Chang Jin, Carsten Eickhoff +2
Large language models (LLMs) rarely admit uncertainty, often producing fluent but misleading answers, rather than abstaining (i.e., refusing to answer). This weakness is even evide…
DCR: Quantifying Data Contamination in LLMs Evaluation
Cheng Xu, Nan Yan, Shuhao Guan +4
The rapid advancement of large language models (LLMs) has heightened concerns about benchmark data contamination (BDC), where models inadvertently memorize evaluation data during t…