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Yingqian Cui

7 accepted papers

2025

A Theoretical Understanding of Chain-of-Thought: Coherent Reasoning and Error-Aware Demonstration

AISTATS 2025poster

Few-shot Chain-of-Thought (CoT) prompting has demonstrated strong performance in improving the reasoning capabilities of large language models (LLMs). While theoretical investigations have been conducted to understand CoT, the underlying transformer used in these studies isolates the CoT reasoning p…

Cited by 0SourceScholar
2025

Six-CD: Benchmarking Concept Removals for Text-to-image Diffusion Models

CVPR 2025poster

Text-to-image (T2I) diffusion models have shown exceptional capabilities in generating images that closely correspond to textual prompts. However, the advancement of T2I diffusion models presents significant risks, as the models could be exploited for malicious purposes, such as generating images wi…

2025

Stepwise Perplexity-Guided Refinement for Efficient Chain-of-Thought Reasoning in Large Language Models

ACL 2025finding

Chain-of-Thought (CoT) reasoning, which breaks down complex tasks into intermediate reasoning steps, has significantly enhanced the performance of large language models (LLMs) on challenging tasks. However, the detailed reasoning process in CoT often incurs long generation times and high computation…

Cited by 0SourcePDFScholar
2025

Superiority of Multi-Head Attention: A Theoretical Study in Shallow Transformers in In-Context Linear Regression

AISTATS 2025poster

We present a theoretical analysis of the performance of transformer with softmax attention in in-context learning with linear regression tasks. While the existing theoretical literature predominantly focuses on providing convergence upper bounds to show that trained transformers with single-/multi-h…

Cited by 0SourceScholar
2024

A Robust Semantics-based Watermark for Large Language Model against Paraphrasing

NAACL 2024findings

Large language models (LLMs) have show their remarkable ability in various natural language tasks. However, there are concerns that LLMs are possible to be used improperly or even illegally. To prevent the malicious usage of LLMs, detecting LLM-generated text becomes crucial in the deployment of LLM…

2024

On the Generalization of Training-based ChatGPT Detection Methods

EMNLP 2024finding

Large language models, such as ChatGPT, achieve amazing performance on various language processing tasks. However, they can also be exploited for improper purposes such as plagiarism or misinformation dissemination. Thus, there is an urgent need to detect the texts generated by LLMs. One type of mos…