Search Results for author:
Found 11 papers, 7 papers with code
Speak Like a Native: Prompting Large Language Models in a Native Style
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
Expression Syntax Information Bottleneck for Math Word Problems
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.
TRIGO: Benchmarking Formal Mathematical Proof Reduction for Generative Language Models
Automated theorem proving (ATP) has become an appealing domain for exploring the reasoning ability of the recent successful generative language models.
DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context Learning
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.
LEGO-Prover: Neural Theorem Proving with Growing Libraries
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)
MATNilm: Multi-appliance-task Non-intrusive Load Monitoring with Limited Labeled Data
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.
A Unifying Framework of Attention-based Neural Load Forecasting
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.
Self-consistent Reasoning For Solving Math Word Problems
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.
A Hybrid Deep Feature-Based Deformable Image Registration Method for Pathology Images
Deformable image registration is a necessary technique for fusing multi-modal pathology slices.
Deep Transfer Learning with Graph Neural Network for Sensor-Based Human Activity Recognition
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.
Attention-based Neural Load Forecasting: A Dynamic Feature Selection Approach
Then, a decoder with hierarchical temporal attention enables a similar day selection, which re-evaluates the importance of historical information at each time step.
