These findings offer a novel perspective on the role of outcome supervision in training verifiers for multi-step reasoning tasks and provide theoretical justification for its advantage in value estimation for planning.
Ranked #16 on Arithmetic Reasoning on GSM8K
no code implementations • 20 Oct 2023 • Xabi Azagirre, Akshay Balwally, Guillaume Candeli, Nicholas Chamandy, Benjamin Han, Alona King, Hyungjun Lee, Martin Loncaric, Sebastien Martin, Vijay Narasiman, Zhiwei, Qin, Baptiste Richard, Sara Smoot, Sean Taylor, Garrett van Ryzin, Di wu, Fei Yu, Alex Zamoshchin
This change was the first documented implementation of a ridesharing matching algorithm that can learn and improve in real time.
Large language models (LLMs) show promise for natural language tasks but struggle when applied directly to complex domains like finance.
1 code implementation • 21 Sep 2023 • Huang Huang, Fei Yu, Jianqing Zhu, Xuening Sun, Hao Cheng, Dingjie Song, Zhihong Chen, Abdulmohsen Alharthi, Bang An, Juncai He, Ziche Liu, Zhiyi Zhang, Junying Chen, Jianquan Li, Benyou Wang, Lian Zhang, Ruoyu Sun, Xiang Wan, Haizhou Li, Jinchao Xu
This paper is devoted to the development of a localized Large Language Model (LLM) specifically for Arabic, a language imbued with unique cultural characteristics inadequately addressed by current mainstream models.
In particular, there is no public common data set for the research field of sound event recognition for the data set of the indoor environmental sound scene.
Statistical analysis of social networks provides valuable insights into complex network interactions across various scientific disciplines.
1 code implementation • 24 May 2023 • Hongbo Zhang, Junying Chen, Feng Jiang, Fei Yu, Zhihong Chen, Jianquan Li, Guiming Chen, Xiangbo Wu, Zhiyi Zhang, Qingying Xiao, Xiang Wan, Benyou Wang, Haizhou Li
Experimental results demonstrate that HuatuoGPT achieves state-of-the-art results in performing medical consultation among open-source LLMs in GPT-4 evaluation, human evaluation, and medical benchmark datasets.
1 code implementation • 20 Apr 2023 • Zhihong Chen, Feng Jiang, Junying Chen, Tiannan Wang, Fei Yu, Guiming Chen, Hongbo Zhang, Juhao Liang, Chen Zhang, Zhiyi Zhang, Jianquan Li, Xiang Wan, Benyou Wang, Haizhou Li
This paper presents our efforts to democratize ChatGPT across language.
This survey paper proposes a clearer view of natural language reasoning in the field of Natural Language Processing (NLP), both conceptually and practically.
However, the current deep learning-based methods face significant challenges in achieving accurate reconstruction with disentangled facial parameters and ensuring temporal stability in single-frame methods for 3D face tracking on video data.
Therefore, we propose a method called region-aware metric learning (RAML), which first separates the regions of the images and generates region-aware features for further metric learning.
This paper proposes an unsupervised cross-modality domain adaptation approach based on pixel alignment and self-training.
Thus, ERNIE-ViL can learn the joint representations characterizing the alignments of the detailed semantics across vision and language.
Ranked #2 on Visual Question Answering (VQA) on VCR (QA-R) test
In this paper, a novel group recommendation method, called attentive geo-social group recommendation, is proposed to recommend the target user with both activity locations and a group of users that may join the activities.
The experimental results show that the PGU-net+ has superior accuracy than the previous state-of-the-art methods on cervical nuclei segmentation.
In recent years, object detection has shown impressive results using supervised deep learning, but it remains challenging in a cross-domain environment.
Segmenting coronary arteries is challenging, as classic unsupervised methods fail to produce satisfactory results and modern supervised learning (deep learning) requires manual annotation which is often time-consuming and can some time be infeasible.
Following the publication of an attack on genome-wide association studies (GWAS) data proposed by Homer et al., considerable attention has been given to developing methods for releasing GWAS data in a privacy-preserving way.