Papers by Jiajie Zeng
LLM-Rec: Personalized Recommendation via Prompting Large Language Models (2024.findings-naacl)
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Hanjia Lyu, Song Jiang, Hanqing Zeng, Yinglong Xia, Qifan Wang, Si Zhang, Ren Chen, Chris Leung, Jiajie Tang, Jiebo Luo
| Challenge: | Recent advances in large language models (LLMs) have showcased their remarkable ability to harness commonsense knowledge and reasoning. |
| Approach: | They propose a novel approach which incorporates four distinct prompting strategies of text enrichment for improving personalized text-based recommendations. |
| Outcome: | The proposed approach improves recommendation quality and even basic MLP models achieve comparable or even better results than complex content-based methods. |
RTADev: Intention Aligned Multi-Agent Framework for Software Development (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) are efficient assistants to humans in software development tasks, but they can cause errors during the development process. |
| Approach: | They propose an intention aligned multi-agent framework that ensures that all agents work based on a consensus. |
| Outcome: | The proposed framework reduces errors and improves the quality of generated software code. |
SeDev: Structured Semantic Exploration for LLM-Driven Code Generation (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) have shown remarkable capabilities in automating code generation, but they suffer from insufficient exploration of the vast solution space. |
| Approach: | They propose a large-scale LLM-driven code generation framework that efficiently finds high-quality solutions in only a few iterations. |
| Outcome: | The proposed framework outperforms baselines while maintaining reasonable time and computational costs. |
LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding (2024.acl-long)
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Yushi Bai, Xin Lv, Jiajie Zhang, Hongchang Lyu, Jiankai Tang, Zhidian Huang, Zhengxiao Du, Xiao Liu, Aohan Zeng, Lei Hou, Yuxiao Dong, Jie Tang, Juanzi Li
| Challenge: | Large language models (LLMs) can only handle texts a few thousand tokens long, limiting their applications on longer sequence inputs, such as books, reports, and codebases. |
| Approach: | They propose a bilingual, multi-task benchmark for long context understanding that extends context windows and more sophisticated memory mechanisms to improve models' long context capabilities. |
| Outcome: | The proposed model outperforms open-source models but struggles on longer contexts. |