1 code implementation • 3 Mar 2024 • Shiqi Chen, Miao Xiong, Junteng Liu, Zhengxuan Wu, Teng Xiao, Siyang Gao, Junxian He
Large language models (LLMs) frequently hallucinate and produce factual errors, yet our understanding of why they make these errors remains limited.
1 code implementation • 16 Jan 2024 • Zida Chen, Ziran Zhang, Haoying Li, Menghao Li, Yueting Chen, Qi Li, Huajun Feng, Zhihai Xu, Shiqi Chen
Both the proposed JARNet and LAP image synthesis pipeline establish a foundation for addressing this intricate challenge.
1 code implementation • NeurIPS 2023 • Shiqi Chen, Yiran Zhao, Jinghan Zhang, I-Chun Chern, Siyang Gao, PengFei Liu, Junxian He
In this benchmark, we collect responses generated from LLMs and annotate factuality labels in a fine-grained manner.
no code implementations • 8 Sep 2023 • Jingwen Zhou, Shiqi Chen, Zheng Ren, Wenguan Zhang, Jiapu Yan, Huajun Feng, Qi Li, Yueting Chen
The joint design of the optical system and the downstream algorithm is a challenging and promising task.
4 code implementations • 25 Jul 2023 • I-Chun Chern, Steffi Chern, Shiqi Chen, Weizhe Yuan, Kehua Feng, Chunting Zhou, Junxian He, Graham Neubig, PengFei Liu
With the above challenges in mind, in this paper, we propose FacTool, a task and domain agnostic framework for detecting factual errors of texts generated by large language models (e. g., ChatGPT).
no code implementations • 28 Jun 2023 • Zheyan Jin, Shiqi Chen, Huajun Feng, Zhihai Xu, Yueting Chen
The procedure is comprehensive, where the similarity of scattered flares and the symmetric effect of reflected ghosts are realized.
no code implementations • 28 Jun 2023 • Zheyan Jin, Shiqi Chen, Yueting Chen, Zhihai Xu, Huajun Feng
Therefore, we propose a framework to integrate large-model prior into low-level computer vision tasks.
2 code implementations • 26 Jun 2023 • Jinghan Zhang, Shiqi Chen, Junteng Liu, Junxian He
In this paper, we propose to compose these parameter-efficient modules through linear arithmetic operations in the weight space, thereby integrating different module capabilities.
2 code implementations • 23 May 2023 • Shiqi Chen, Siyang Gao, Junxian He
Detecting factual errors in summaries has been an important and challenging subject in summarization research.
no code implementations • 10 May 2023 • Shiqi Chen, Ting Lin, Huajun Feng, Zhihai Xu, Qi Li, Yueting Chen
Correcting the optical aberrations and the manufacturing deviations of cameras is a challenging task.
no code implementations • 10 May 2023 • Shiqi Chen, Jinwen Zhou, Menghao Li, Yueting Chen, Tingting Jiang
In digital images, the performance of optical aberration is a multivariate degradation, where the spectral of the scene, the lens imperfections, and the field of view together contribute to the results.
1 code implementation • 10 May 2023 • Shiqi Chen, Huajun Feng, Dexin Pan, Zhihai Xu, Qi Li, Yueting Chen
Due to the spatial variation in optical aberrations, which cannot be avoided during the lens design process, recent commercial cameras have shifted some of these correction tasks from optical design to postprocessing systems.
no code implementations • 16 Mar 2023 • Zheyan Jin, Shiqi Chen, Huajun Feng, Zhihai Xu, Qi Li, Yueting Chen
In order to obtain real shot data in different scenes, we used fog generators, array cameras, mobile phones, underwater cameras and drones to obtain haze data.
1 code implementation • Optics Express 2022 • Ting Lin, Shiqi Chen, Huajun Feng, Zhihai Xu, Qi Li, Yueting Chen
In mobile photography applications, limited volume constraints the diversity of optical design.
no code implementations • 27 Apr 2021 • Shiqi Chen, Zhengyu Chen, Donglin Wang
Meta Reinforcement Learning (MRL) enables an agent to learn from a limited number of past trajectories and extrapolate to a new task.
1 code implementation • ICCV 2021 • Shiqi Chen, Huajun Feng, Keming Gao, Zhihai Xu, Yueting Chen
To meet the space limitation of optical elements, free-form surfaces or high-order aspherical lenses are adopted in mobile cameras to compress volume.