no code implementations • 14 Mar 2024 • Ante Wang, Linfeng Song, Ye Tian, Baolin Peng, Lifeng Jin, Haitao Mi, Jinsong Su, Dong Yu
Calibration, which establishes the correlation between accuracy and model confidence, is important for LLM development.
no code implementations • 2 Mar 2024 • Jianheng Huang, Leyang Cui, Ante Wang, Chengyi Yang, Xinting Liao, Linfeng Song, Junfeng Yao, Jinsong Su
When conducting continual learning based on a publicly-released LLM checkpoint, the availability of the original training data may be non-existent.
no code implementations • 23 Feb 2024 • Ante Wang, Linfeng Song, Baolin Peng, Ye Tian, Lifeng Jin, Haitao Mi, Jinsong Su, Dong Yu
Experiments on Biographies show that our method can effectively improve the factuality of generations with simple and intuitive prompts across different scales of LLMs.
1 code implementation • 20 Dec 2023 • Jianheng Huang, Ante Wang, Linfeng Gao, Linfeng Song, Jinsong Su
Based on the observation that the search query is typically related to the topic of dialogue response, we train a response-augmented query producer (RA) to provide rich and effective training signals for QP.
1 code implementation • 16 Feb 2023 • Ante Wang, Linfeng Song, Qi Liu, Haitao Mi, Longyue Wang, Zhaopeng Tu, Jinsong Su, Dong Yu
We propose a dialogue model that can access the vast and dynamic information from any search engine for response generation.
1 code implementation • EMNLP 2021 • Shaopeng Lai, Ante Wang, Fandong Meng, Jie zhou, Yubin Ge, Jiali Zeng, Junfeng Yao, Degen Huang, Jinsong Su
Dominant sentence ordering models can be classified into pairwise ordering models and set-to-sequence models.
no code implementations • ACL 2021 • Yubin Ge, Ly Dinh, Xiaofeng Liu, Jinsong Su, Ziyao Lu, Ante Wang, Jana Diesner
In this paper, we focus on the problem of citing sentence generation, which entails generating a short text to capture the salient information in a cited paper and the connection between the citing and cited paper.
1 code implementation • ACL 2020 • Linfeng Song, Ante Wang, Jinsong Su, Yue Zhang, Kun Xu, Yubin Ge, Dong Yu
The task of graph-to-text generation aims at producing sentences that preserve the meaning of input graphs.
Ranked #10 on Data-to-Text Generation on WebNLG