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پستهای کانال
Pythagorean traingle and $\pi$
The area of a circle inscribed in a Pythagorean triangle is $\pi$.
| 2 |  +1Context-Aware Code Generation
In practice, you rarely need to write code taken out of context. As a rule, we write code that becomes part of the project. This code is closely related to the project ideologically, syntactically and stylistically. How to ensure this when generating code using LLM? If the project is small, then the entire project code can be passed as context. For a large project, this trick will not work, and such approaches as RAG (this one or this one) are required to fetch relevant information from existing code repositories and use it to create accurate code, documentation, or even fix code errors. | 194 |
| 3 | +1 Improving LoRA: Implementing Weight-Decomposed Low-Rank Adaptation (DoRA) from Scratch
Recently researchers proposed DoRA: Weight-Decomposed Low-Rank Adaptation. DoRA decomposes the pre-trained weight into two components, magnitude and direction, for fine-tuning, specifically employing LoRA for directional updates to efficiently minimize the number of trainable parameters. DoRA consistently outperforms LoRA on fine-tuning LLaMA, LLaVA, and VL-BART on various downstream tasks, such as commonsense reasoning, visual instruction tuning, and image/video-text understanding
To understand how these methods Sebastian Raschka in his article suggests implementing both LoRA and DoRA in PyTorch from scratch. | 202 |
| 4 | What is a "bug"?
The authors analyze various definitions of the term "bug". | 270 |
| 5 | LLM as OS, Agents as Apps: Envisioning AIOS, Agents and the AIOS-Agent Ecosystem
The paper presents a vision for the future of computing within the AIOS-Agent ecosystem, where the LLM functions as the core of AIOS (Artificial Intelligent Operating System). | 232 |
| 6 | +1 BAFLineDP: Code Bilinear Attention Fusion Framework for Line-Level Defect Prediction
The paper presents a line-level defect prediction method grounded in a code bilinear attention fusion framework (BAFN).
BAFN is designed to amalgamate global and local information by capturing the bilinear interaction attention weights between code lines and their respective line-level contextual information to construct defect code features.
github: https://github.com/insoft-lab/BAFLineDP | 201 |
| 7 | Mercury: An Efficiency Benchmark for LLM Code Synthesis
While LLMs demonstrate the remarkable capability to generate functionally correct code, there still exists a substantial gap in their efficiency output.
Mercury is a benchmark designated for assessing the code efficiency of LLM code synthesis tasks. It consists of 1,889 programming tasks covering diverse difficulty levels.
Each Mercury task includes a task description, a test case generator, a prompt & entry point, and corresponding solutions.
github: https://github.com/Elfsong/Mercury | 183 |
| 8 | +1 Collaborative Agents for Software Engineering
CodeAgent is a novel multi-agent-based system for code review. To demonstrate the effectiveness of CodeAgent, the authors performed experiments to assess its capabilities in various tasks including
- detection of inconsistencies between code changes and commit messages
- detection of vulnerability introduction by commits
- validation of adherence to code style.
Website: https://code-agent-new.vercel.app/index.html | 224 |
| 9 | StepCoder: Improve Code Generation with Reinforcement Learning from Compiler Feedback
StepCoder is a novelty training framework for code generation via RL. It breaks down complicated exploration problems to reduce the difficulty of exploring environments with sparse rewards while providing fine-grained optimization. In addition, the authors constructed a high-quality dataset APPS+, specifically for code generation.
Dataset: https://github.com/Ablustrund/APPS_Plus | 250 |
| 10 | +1 GitBug-Java: A Reproducible Benchmark of Recent Java Bugs
The authors introduce GitBug-Java, a reproducible benchmark of recent Java bugs featuring 199 bug-fixes sourced from 55 relevant open-source repositories. To ensure the relevance of the bug-fixes to current development practices, they only collected bug-fixes from 2023. This may be useful in LLM evaluations.
GitBug-Java also provides offline reproduction environments for each collected bug-fix. To guarantee the validity and quality of the bug-fixes included in GitBug-Java, the authors manually curated the included bug-fixes.
site: https://www.nuno.saavedra.pt/gitbug-java/#!/
github: https://github.com/gitbugactions/gitbug-java | 341 |
| 11 | JetBrains' unremovable AI assistant meets irresistible outcry
Some JetBrains customers feel strongly about AI Assistant and really don't want the plugin to be present in their JetBrains applications at all, whether that's due to corporate policies that are incompatible with AI Assistant or other concerns. But because the plugin code has been "deeply integrated," removal has proven complicated.
More than dozen threads on JetBrains' YouTrack issue board have been posted seeking a way to delete, uninstall, or otherwise excise the AI Assistant plugin since it debuted. | 321 |
| 12 | Meta AI releases Code Llama 70B
The model is designed for general code synthesis and understanding.
Weights: https://huggingface.co/codellama/CodeLlama-70b-hf | 423 |
| 13 | +1 DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence
Paper is added. | 337 |
| 14 | +2 Investigating the Efficacy of Large Language Models for Code Clone Detection (CCD)
The authors investigated the applicability of LLMs for CCD (Type-4 code clones).
RQs:
- What is the effect of different prompts to encourage Chat-GPT to identify Code Clones?
- What is the performance of ChatGPT for code clone detection compared to the baselines (CodeBERT, RoBERTa, GraphCodeBERT)?
ChatGPT (GPT-3.5-turbo) surpasses the baselines in cross-language CCD attaining an F1-score of 0.877 and achieves comparable performance to fully fine-tuned models for mono-lingual CCD, with an F1-score of 0.878. | 404 |
| 15 | The behavior of a Tesla vehicle will be determined by artificial intelligence, rather than coded by engineers
FSD Beta v12 upgrades the city-streets driving stack to a single end-to-end neural network trained on millions of video clips, replacing over 300k lines of explicit C++ code. | 242 |
| 16 | Synergy of Reinforcement Learning and Large Language Models (RL+LLMs) @ AAAI 2024
The goal of the workshop is to bring together RL and LLM communities to facilitate cross-pollination.
Workshop: February 26th 2024
Accepted papers:
- Suspicion-Agent: Playing Imperfect Information Games with Theory of Mind Aware GPT-4
- Generate Subgoal Images before Act: Unlocking the Chain-of-Thought Reasoning in Diffusion Model for Robot Manipulation with Multimodal Prompts
- CriticGPT: Multimodal LLM as a Critic for Robot Manipulation
- Decision Transformer With Tokenized Actions
- Reinforcement Learning for Optimizing RAG for Domain Chatbots
- Software Security Vulnerability Repair Using Reinforcement Learning with Large Language Models
- Exploring Reinforcement Learning with Large Language Models for Enhancing Badminton Players' Strategies
- DeLF: Designing Learning Environments with Foundation Models | 451 |
| 17 | Committing without git
How to create a branch with two commits (add file and change file) without running git.
Source: https://matheustavares.gitlab.io/assets/committing-without-git/commit.py | 380 |
| 18 | Soaring from 4K to 400K: Extending LLM's Context with Activation Beacon
The work introduces Activation Beacon for the extension of LLM’s context length. Activation Beacon condenses the LLM’s raw activations into more compact forms, enabling the LLM to perceive a vast context with a limited context window. As a plug-and-play component for the LLM, it brings in long contextual information while fully preserving the LLM’s existing capabilities on short contexts. The experimental studies show that Activation Beacon is able to extend Llama-2-7B's context length by x100 times (from 4K to 400K), meanwhile achieving a superior result on both long-context generation and understanding tasks.
GitHub: https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon | 385 |
| 19 | https://www.theverge.com/2024/1/4/24023809/microsoft-copilot-key-keyboard-windows-laptops-pcs | 341 |
| 20 | +1 Generative AI for Math: Part I -- MathPile: A Billion-Token-Scale Pretraining Corpus for Math
MathPile is a specialized corpus centered around mathematics, characterized by its diversity and high quality. The authors plan to open-source different versions of MathPile with the scripts used for processing, to facilitate future developments in this field.
GitHub: https://github.com/GAIR-NLP/MathPile/ | 387 |
