Search Results for author: Jing Xiong

Found 12 papers, 8 papers with code

CAPE: Context-Adaptive Positional Encoding for Length Extrapolation

1 code implementation23 May 2024 Chuanyang Zheng, Yihang Gao, Han Shi, Minbin Huang, Jingyao Li, Jing Xiong, Xiaozhe Ren, Michael Ng, Xin Jiang, Zhenguo Li, Yu Li

Positional encoding plays a crucial role in transformers, significantly impacting model performance and length generalization.

Speak Like a Native: Prompting Large Language Models in a Native Style

1 code implementation22 Nov 2023 Zhicheng Yang, Yiwei Wang, Yinya Huang, Jing Xiong, Xiaodan Liang, Jing Tang

Specifically, with AlignedCoT, we observe an average +3. 2\% improvement for \texttt{gpt-3. 5-turbo} compared to the carefully handcrafted CoT on multi-step reasoning benchmarks. Furthermore, we use AlignedCoT to rewrite the CoT text style in the training set, which improves the performance of Retrieval Augmented Generation by 3. 6\%. The source code and dataset is available at https://github. com/yangzhch6/AlignedCoT

Common Sense Reasoning GSM8K +3

Expression Syntax Information Bottleneck for Math Word Problems

1 code implementation24 Oct 2023 Jing Xiong, Chengming Li, Min Yang, Xiping Hu, Bin Hu

To this end, we design an Expression Syntax Information Bottleneck method for MWP (called ESIB) based on variational information bottleneck, which extracts essential features of expression syntax tree while filtering latent-specific redundancy containing syntax-irrelevant features.


DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context Learning

1 code implementation4 Oct 2023 Jing Xiong, Zixuan Li, Chuanyang Zheng, Zhijiang Guo, Yichun Yin, Enze Xie, Zhicheng Yang, Qingxing Cao, Haiming Wang, Xiongwei Han, Jing Tang, Chengming Li, Xiaodan Liang

Dual Queries first query LLM to obtain LLM-generated knowledge such as CoT, then query the retriever to obtain the final exemplars via both question and the knowledge.

Dimensionality Reduction In-Context Learning +1

LEGO-Prover: Neural Theorem Proving with Growing Libraries

1 code implementation1 Oct 2023 Haiming Wang, Huajian Xin, Chuanyang Zheng, Lin Li, Zhengying Liu, Qingxing Cao, Yinya Huang, Jing Xiong, Han Shi, Enze Xie, Jian Yin, Zhenguo Li, Heng Liao, Xiaodan Liang

Our ablation study indicates that these newly added skills are indeed helpful for proving theorems, resulting in an improvement from a success rate of 47. 1% to 50. 4%.

 Ranked #1 on Automated Theorem Proving on miniF2F-test (Pass@100 metric)

Automated Theorem Proving

MATNilm: Multi-appliance-task Non-intrusive Load Monitoring with Limited Labeled Data

1 code implementation27 Jul 2023 Jing Xiong, Tianqi Hong, Dongbo Zhao, Yu Zhang

Non-intrusive load monitoring (NILM) identifies the status and power consumption of various household appliances by disaggregating the total power usage signal of an entire house.

energy management Non-Intrusive Load Monitoring

A Unifying Framework of Attention-based Neural Load Forecasting

1 code implementation8 May 2023 Jing Xiong, Yu Zhang

In this paper, we propose a unifying deep learning framework for load forecasting, which includes time-varying feature weighting, hierarchical temporal attention, and feature-reinforced error correction.

Decoder Load Forecasting

Self-consistent Reasoning For Solving Math Word Problems

no code implementations27 Oct 2022 Jing Xiong, Zhongwei Wan, Xiping Hu, Min Yang, Chengming Li

Specifically, we firstly obtain a sub-network by pruning a roberta2tree model, for the sake to use the gap on output distribution between the original roberta2tree model and the pruned sub-network to expose spurious correlative samples.


Deep Transfer Learning with Graph Neural Network for Sensor-Based Human Activity Recognition

no code implementations14 Mar 2022 Yan Yan, Tianzheng Liao, Jinjin Zhao, Jiahong Wang, Liang Ma, Wei Lv, Jing Xiong, Lei Wang

Given this observation, we devised a graph-inspired deep learning approach toward the sensor-based HAR tasks, which was further used to build a deep transfer learning model toward giving a tentative solution for these two challenging problems.

Few-Shot Learning Graph Neural Network +2

Attention-based Neural Load Forecasting: A Dynamic Feature Selection Approach

no code implementations25 Aug 2021 Jing Xiong, Pengyang Zhou, Alan Chen, Yu Zhang

Then, a decoder with hierarchical temporal attention enables a similar day selection, which re-evaluates the importance of historical information at each time step.

Decoder feature selection +5

Cannot find the paper you are looking for? You can Submit a new open access paper.