4 papers
Chronologic: Measuring Language Models' Ability to Represent the Past
Ted Underwood, Ziliang Qiu, Sarah Griebel +4
Language models are appealing tools for research on the past. But to trust the evidence a model provides, researchers need to know whether its responses fit the period represented.…
Creative Convergence or Imitation? Genre-Specific Homogeneity in LLM-Generated Chinese Literature
Yuanchi Ma, Kaize Shi, Hui He +5
Large Language Models (LLMs) have demonstrated remarkable capabilities in narrative generation. However, they often produce structurally homogenized stories, frequently following r…
Learning Self-Correction in Vision-Language Models via Rollout Augmentation
Yi Ding, Ziliang Qiu, Bolian Li +1
Self-correction is essential for solving complex reasoning problems in vision-language models (VLMs). However, existing reinforcement learning (RL) methods struggle to learn it, as…
Deep Associations, High Creativity: A Simple yet Effective Metric for Evaluating Large Language Models
Ziliang Qiu, Renfen Hu
The evaluation of LLMs' creativity represents a crucial research domain, though challenges such as data contamination and costly human assessments often impede progress. Drawing in…