Papers by Mingmeng Geng
code-transformed: The Influence of Large Language Models on Code (2026.findings-eacl)
Copied to clipboard
| Challenge: | Using Large Language Models, code generation capabilities have transformed programming practices. |
| Approach: | They analyze 20,000 GitHub repositories linked to arXiv papers published between 2020 and 2025 . they identify measurable trends in the evolution of coding style that align with LLM-generated code . |
| Outcome: | The proposed study examines 20,000 GitHub repositories linked to arXiv papers . it finds that LLMs influence code style, and that they can be observed in real-world code . |
LLM as a Broken Telephone: Iterative Generation Distorts Information (2025.acl-long)
Copied to clipboard
| Challenge: | Large language models are increasingly responsible for online content, but they can be distorted by repeated transmission. |
| Approach: | They investigate whether large language models distort information through iterative generation. |
| Outcome: | The findings raise important questions about the reliability of LLM-generated content in iterative workflows. |
The Impact of Large Language Models in Academia: from Writing to Speaking (2025.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) are impacting human society, especially in textual information. |
| Approach: | They propose to build an automated monitoring platform to track the impact of large language models on human expression. |
| Outcome: | The results show that LLM-style words such as significant are used more frequently in abstracts and oral presentations. |
Human-LLM Coevolution: Evidence from Academic Writing (2025.findings-acl)
Copied to clipboard
| Challenge: | a statistical analysis of arXiv paper abstracts shows a marked drop in the frequency of several words previously identified as overused by ChatGPT, such as “delve”, starting soon after they were pointed out in early 2024. |
| Approach: | They report a drop in the frequency of several words previously identified as overused by ChatGPT, such as “delve”, starting soon after they were pointed out in early 2024. |
| Outcome: | The frequency of words previously identified as overused by ChatGPT, such as “delve”, has instead kept increasing. |