Currently, although Large Language Models (LLMs) have shown significant performance in the field of code generation, their effectiveness in handling complex programming tasks remains limited. This is primarily due to the substantial distance between the problem description and the correct code, making ...
TL;DR在LLM代码生成中使用Planning方法。之前的LLM在decoder生成的结果中,会使用Beam Search这类算法来生成最终的代码,但是这种算法貌似不适用于代码生成,生成的代码经常CE/输出错误。因此作者提出了一种Planni…
Language-to-code Language-to-language State-of-the-Art AI Foundation Models Large language models(LLMs) are hard to develop and maintain, requiring mountains of data, significant investment, technical expertise, and massive-scale compute infrastructure. Starting with one of NeMo’s pretrained foundati...
[文献翻译] Retrieval-Augmented Generation for Natural Language Processing: A Survey 自然语言处理中的检索增强生成RAG:综述摘要大语言模型 (LLMs) 在各个领域都取得了巨大的成功,这得益于它们存储知识的巨大参数量。然而,LLMs 仍然存在一些关键问题,例如幻觉问题、知识更… 李常青 文章分享 Large Language Models:...
Code Large Language Models (Code LLMs), such as StarCoder, have demonstrated exceptional performance in code-related tasks. However, most existing models are solely pre-trained on extensive raw code data without instruction fine-tuning. In this paper, we introduce WizardC...
[全网首发中文版]LLM4Decompile: Decompiling Binary Code with Large Language Models,反编译是将已编译的机器代码或字节码转换回高级编程语言的过程。当源代码无法访问时,通常会这样做来分析软件的工作原理Brumley等人(2013);Katz等人(2018);胡赛尼和多兰-加维特(2022)
Additionally, the emergence and adoption of retrieval-augmented generation (RAG) is helping LLMs deliver more-accurate and relevant AI responses. In the RAG methodology, foundational large language models are connected to knowledge bases—often company-specific, proprietary data—to inject up-to-date,...
OpenAI releasedGPT-3, a 175 billion-parameter model that generated text and code with short written prompts.In 2021, NVIDIA and Microsoft developed Megatron-Turing Natural Language Generation 530B, one of the world’s largest models for reading comprehension and natural language inference, with 530...
generation task, the instruction-tuned models demonstrate only marginal improvements compared to the base models. Third, we disclose the types of failure modes that exist in our evaluation results. All these results underscore the need for further advancements in self-invoking code generation tasks ...
there is little work on investigating whether these models can effectively interpret visual elements for code generation. To this end, we present MMCode, the first multi-modal coding dataset for evaluating algorithmic problem-solving skills in visually rich contexts. MMCode contains 3,548 questions ...