Papers by Yiyang Cai
Instance-level Randomization: Toward More Stable LLM Evaluations (2025.findings-emnlp)
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| Challenge: | Evaluations of large language models suffer from instability, where small changes of random factors can lead to drastic fluctuations of scores and even model rankings. |
| Approach: | They propose an instance-level randomization method to reduce variance and improve fairness in evaluations by randomizing all factors that affect evaluation scores for every single instance. |
| Outcome: | The proposed method reduces variance and improves fairness in model comparisons by using instance-level randomization. |
CADMate: Generating CAD Assembly Plan with Geometric Chain-of-Thought and Spatial Physical Rewards (2026.acl-long)
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| Challenge: | Computer-aided design (CAD) is crucial in prototyping complex 3D objects . designers manually define assembly sequences for individual CAD parts . |
| Approach: | They propose a framework for computer-aided design that predicts actions for CAD parts . they use a reference design image and disassembled parts to generate 6-DoF transformations . |
| Outcome: | The proposed framework outperforms existing MLLMs in the design of CAD assemblies. |
Uplift-RAG: Uplift-Driven Knowledge Preference Alignment for Retrieval-Augmented Generation (2025.findings-emnlp)
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| Challenge: | Existing efforts to estimate document utility rely on downstream generation performance, which conflates the influence of external documents with the intrinsic knowledge of the LLM. |
| Approach: | They propose an uplift-based definition of document utility that quantifies each document’s marginal benefit over the LLM’s internal knowledge. |
| Outcome: | The proposed framework improves the performance of the LLM by incorporating external retrieved documents into the model. |
Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have demonstrated exceptional proficiency in language-related tasks, but their deployment poses significant memory and storage requirements. |
| Approach: | They propose a method that optimizes rounding values and weight clipping within 200 steps. |
| Outcome: | The proposed method achieves exceptional results across 2 to 4 bits while maintaining low tuning costs and avoiding additional inference overhead. |
Graceful Forgetting in Generative Language Models (2025.emnlp-main)
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| Challenge: | Recent studies show that pre-trained models do not provide all knowledge needed for fine-tuning tasks. |
| Approach: | They propose a framework to achieve graceful forgetting in generative language models by pre-training a model on large-scale correlating datasets. |
| Outcome: | The proposed framework improves the learning plasticity of the target task by selectively discarding irrelevant knowledge. |