DeTikZify: A Novel Multimodal Language Model for Synthesizing TikZ Graphics Programs from Scientific Figures and Sketches
DeTikZify, a novel multimodal language model, effectively synthesizes TikZ programs for scientific figures and sketches, outperforming existing commercial large language models and demonstrating the potential of MCTS for iterative refinement in code generation.
DemoCraft: A System for Improving Code Generation in Large Language Models Using In-Context Learning and Latent Concept Learning
DemoCraft improves the accuracy and functionality of large language models (LLMs) in generating code from natural language instructions by using latent concept learning to select relevant demonstrations for in-context learning.
프로그래밍 지식 그래프를 활용한 컨텍스트 기반 코드 생성: 검색 성능 향상 및 환각 현상 감소
대규모 언어 모델(LLM) 기반 코드 생성 성능을 향상시키기 위해, 본 논문에서는 프로그래밍 지식 그래프(PKG)를 활용한 새로운 컨텍스트 기반 코드 생성 프레임워크를 제안합니다. PKG는 코드 검색의 정확도를 높이고, 트리 가지치기 기법을 통해 관련 없는 정보를 줄여 환각 현상을 감소시킵니다.
Context-Aware Code Generation with Programming Knowledge Graphs: A Novel Approach
This research paper introduces a novel framework for enhancing code generation using Programming Knowledge Graphs (PKG) and a re-ranking mechanism to improve the accuracy and relevance of generated code.
MojoBench: A Framework for Evaluating and Improving Mojo Code Generation in Large Language Models
Large language models (LLMs) often struggle with code generation in emerging programming languages like Mojo. MojoBench introduces a novel framework, including a benchmark dataset and specialized LLMs, to evaluate and enhance Mojo code generation capabilities, highlighting the importance of domain-specific pretraining and targeted finetuning.
CONAN: A Retrieval-Augmented Language Model for Code Assistance
CONAN, a novel retrieval-augmented language model, effectively assists code generation, summarization, and completion by leveraging a structure-aware retriever and a dual-view code representation mechanism.
Meta's RLEF Achieves State-of-the-Art Coding Performance by Fine-Tuning LLMs with Execution Feedback
Meta's novel RLEF method significantly improves code generation capabilities of Large Language Models (LLMs) by leveraging execution feedback, achieving state-of-the-art results and surpassing even GPT-4 in efficiency and accuracy.
RTLCoder: An Open-Source LLM for Efficient RTL Code Generation
RTLCoder is a novel, open-source, and efficient large language model (LLM) specifically designed for generating RTL code from natural language instructions, outperforming existing commercial and open-source solutions in accuracy and efficiency.
Horizon-Length Prediction: Improving Code Generation by Predicting Completion Length
Current code language models struggle with accurately filling in missing code because they lack the ability to plan ahead. This paper introduces Horizon-Length Prediction (HLP), a novel training objective that teaches models to predict the length of the missing code, significantly improving their ability to generate coherent and accurate code completions.
Learning to Self-Refine Code Generation with Cycle Framework
Pre-trained code language models struggle with self-refinement, but Cycle framework enhances self-refinement capability, improving code generation performance.
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