38 citations · 43 across the 7 of their papers we have counts for
7 papers
HiCaM: A Hierarchical-Causal Modification Framework for Long-Form Text Modification
Yuntao Shi, Yi Luo, Yeyun Gong +1
Large Language Models (LLMs) have achieved remarkable success in various domains. However, when handling long-form text modification tasks, they still face two major problems: (1)…
Generalized Category Discovery in Event-Centric Contexts: Latent Pattern Mining with LLMs
Yi Luo, Qiwen Wang, Junqi Yang +5
Generalized Category Discovery (GCD) aims to classify both known and novel categories using partially labeled data that contains only known classes. Despite achieving strong perfor…
From Intention To Implementation: Automating Biomedical Research via LLMs
Yi Luo, Linghang Shi, Yihao Li +4
Conventional biomedical research is increasingly labor-intensive due to the exponential growth of scientific literature and datasets. Artificial intelligence (AI), particularly Lar…
Infant Agent: A Tool-Integrated, Logic-Driven Agent with Cost-Effective API Usage
Bin Lei, Yuchen Li, Yiming Zeng +7
Despite the impressive capabilities of large language models (LLMs), they currently exhibit two primary limitations, \textbf{\uppercase\expandafter{\romannumeral 1}}: They struggle…
Revolutionizing Database Q&A with Large Language Models: Comprehensive Benchmark and Evaluation
Yihang Zheng, Bo Li, Zhenghao Lin +6
The development of Large Language Models (LLMs) has revolutionized QA across various industries, including the database domain. However, there is still a lack of a comprehensive be…
Ensuring Safe and High-Quality Outputs: A Guideline Library Approach for Language Models
Yi Luo, Zhenghao Lin, Yuhao Zhang +7
Large Language Models (LLMs) exhibit impressive capabilities but also present risks such as biased content generation and privacy issues. One of the current alignment techniques in…
Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models
Jiashuo Sun, Yi Luo, Yeyun Gong +4
Large language models (LLMs) can achieve highly effective performance on various reasoning tasks by incorporating step-by-step chain-of-thought (CoT) prompting as demonstrations. H…