38 citations · 144 across the 13 of their papers we have counts for
13 papers
Focus Directions Make Your Language Models Pay More Attention to Relevant Contexts
Youxiang Zhu, Ruochen Li, Danqing Wang +2
Long-context large language models (LLMs) are prone to be distracted by irrelevant contexts. The reason for distraction remains poorly understood. In this paper, we first identify…
End-to-end Story Plot Generator
Hanlin Zhu, Andrew Cohen, Danqing Wang +4
Story plots, while short, carry most of the essential information of a full story that may contain tens of thousands of words. We study the problem of automatic generation of story…
ALGO: Synthesizing Algorithmic Programs with LLM-Generated Oracle Verifiers
Kexun Zhang, Danqing Wang, Jingtao Xia +2
Large language models (LLMs) excel at implementing code from functionality descriptions but struggle with algorithmic problems that require not only implementation but also identif…
Learning from Mistakes via Cooperative Study Assistant for Large Language Models
Danqing Wang, Lei Li
Large language models (LLMs) have demonstrated their potential to refine their generation based on their own feedback. However, the feedback from LLM itself is often inaccurate, th…
Accelerating Antimicrobial Peptide Discovery with Latent Structure
Danqing Wang, Zeyu Wen, Fei Ye +2
Antimicrobial peptides (AMPs) are promising therapeutic approaches against drug-resistant pathogens. Recently, deep generative models are used to discover new AMPs. However, previo…
MTG: A Benchmark Suite for Multilingual Text Generation
Yiran Chen, Zhenqiao Song, Xianze Wu +5
We introduce MTG, a new benchmark suite for training and evaluating multilingual text generation. It is the first-proposed multilingual multiway text generation dataset with the la…