Search Results for author:
Found 19 papers, 11 papers with code
DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data
Proof assistants like Lean have revolutionized mathematical proof verification, ensuring high accuracy and reliability.
DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
MLA guarantees efficient inference through significantly compressing the Key-Value (KV) cache into a latent vector, while DeepSeekMoE enables training strong models at an economical cost through sparse computation.
Reading Users' Minds from What They Say: An Investigation into LLM-based Empathic Mental Inference
In human-centered design, developing a comprehensive and in-depth understanding of user experiences, i. e., empathic understanding, is paramount for designing products that truly meet human needs.
DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
Mathematical reasoning poses a significant challenge for language models due to its complex and structured nature.
Ranked #12 on
Math Word Problem Solving
on MATH
(using extra training data)
DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence
The rapid development of large language models has revolutionized code intelligence in software development.
Ranked #11 on
Code Generation
on MBPP
DeepSeek LLM: Scaling Open-Source Language Models with Longtermism
The rapid development of open-source large language models (LLMs) has been truly remarkable.
Toward Artificial Empathy for Human-Centered Design: A Framework
Specifically, we conduct an interdisciplinary investigation of research areas such as data-driven user studies, empathic understanding development, and artificial empathy.
Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers
This paper proposes a generative design approach based on the generative pre-trained language model (PLM) to automatically retrieve and map biological analogy and generate BID in the form of natural language.
Generative Transformers for Design Concept Generation
Generating novel and useful concepts is essential during the early design stage to explore a large variety of design opportunities, which usually requires advanced design thinking ability and a wide range of knowledge from designers.
Generative Pre-Trained Transformers for Biologically Inspired Design
Biological systems in nature have evolved for millions of years to adapt and survive the environment.
Generative Design Ideation: A Natural Language Generation Approach
This paper aims to explore a generative approach for knowledge-based design ideation by applying the latest pre-trained language models in artificial intelligence (AI).
Generative Pre-Trained Transformer for Design Concept Generation: An Exploration
Novel concepts are essential for design innovation and can be generated with the aid of data stimuli and computers.
Lyra: A Benchmark for Turducken-Style Code Generation
Since a declarative language is typically embedded in an imperative language (i. e., the turducken-style programming) in real-world software development, the promising results on declarative languages can hardly lead to significant reduction of manual software development efforts.
A Syntax-Guided Edit Decoder for Neural Program Repair
Our results show that Recoder repairs 53 bugs on Defects4J v1. 2, which achieves 21. 4% improvement over the previous state-of-the-art approach for single-hunk bugs (TBar).
Generalized Equivariance and Preferential Labeling for GNN Node Classification
Existing graph neural networks (GNNs) largely rely on node embeddings, which represent a node as a vector by its identity, type, or content.
OCoR: An Overlapping-Aware Code Retriever
To address these problems, we propose a novel neural architecture named OCoR, where we introduce two specifically-designed components to capture overlaps: the first embeds identifiers by character to capture the overlaps between identifiers, and the second introduces a novel overlap matrix to represent the degrees of overlaps between each natural language word and each identifier.
NLocalSAT: Boosting Local Search with Solution Prediction
However, in this method, the initialization is assigned in a random manner, which impacts the effectiveness of SLS solvers.
TreeGen: A Tree-Based Transformer Architecture for Code Generation
TreeGen outperformed the previous state-of-the-art approach by 4. 5 percentage points on HearthStone, and achieved the best accuracy among neural network-based approaches on ATIS (89. 1%) and GEO (89. 6%).
A Grammar-Based Structural CNN Decoder for Code Generation
In this paper, we propose a grammar-based structural convolutional neural network (CNN) for code generation.
