2 citations · 5 across the 13 of their papers we have counts for
6 papers · 1 filter
Self-Exploring Language Models for Explainable Link Forecasting on Temporal Graphs via Reinforcement Learning
Zifeng Ding, Shenyang Huang, Zeyu Cao +11
Forecasting future links is a central task in temporal graph (TG) reasoning, requiring models to leverage historical interactions to predict upcoming ones. Traditional neural appro…
Toward Reliable Clinical Coding with Language Models: Verification and Lightweight Adaptation
Zhangdie Yuan, Han-Chin Shing, Mitch Strong +1
Accurate clinical coding is essential for healthcare documentation, billing, and decision-making. While prior work shows that off-the-shelf LLMs struggle with this task, evaluation…
Do Language Models Update their Forecasts with New Information?
Zhangdie Yuan, Zifeng Ding, Andreas Vlachos
Prior work has largely treated future event prediction as a static task, failing to consider how forecasts should evolve as new evidence emerges. To address this gap, we introduce…
TCP: a Benchmark for Temporal Constraint-Based Planning
Zifeng Ding, Sikuan Yan, Zhangdie Yuan +3
Temporal reasoning and planning are essential capabilities for large language models (LLMs), yet most existing benchmarks evaluate them in isolation and under limited forms of comp…
Capturing Symmetry and Antisymmetry in Language Models through Symmetry-Aware Training Objectives
Zhangdie Yuan, Andreas Vlachos
Capturing symmetric (e.g., country borders another country) and antisymmetric (e.g., parent_of) relations is crucial for a variety of applications. This paper tackles this challeng…
FOReCAst: The Future Outcome Reasoning and Confidence Assessment Benchmark
Zhangdie Yuan, Zifeng Ding, Andreas Vlachos
Forecasting is an important task in many domains, such as technology and economics. However existing forecasting benchmarks largely lack comprehensive confidence assessment, focus…