ml4se

LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models LongLoRA, an efficient fine-tuning approach that extends the context sizes of pre-trained LLMs, with limited computation cost. It adopts LLaMA2 7B from 4k context to 100k, or LLaMA2 70B to 32k on a single 8xA100 machine. Results: 1. The proposed shifted short attention is easy to implement, compatible with Flash-Attention, and not required during inference. 2. Released models: from 7B to 70B, context length from 8k to 100k, including LLaMA2-LongLoRA-7B-100k, LLaMA2-LongLoRA-13B-64k, and LLaMA2-LongLoRA-70B-32k. 3. A long-context QA dataset, LongQA, for supervised fine-tuning (SFT). Repository: https://github.com/dvlab-research/LongLoRA

Communicative Agents for Software Development ChatDev is a chat-based end-to-end software development framework that leverages LLMs to facilitate effective communication and collaboration among multiple roles involved in the software development process. By decomposing the development process into sequential atomic subtasks through the use of the chat chain, ChatDev enables granular focus and promotes desired outputs for each subtask. The experimental results demonstrate the efficiency and cost-effectiveness of the automated software development process driven by ChatDev. The Human-Agent-Interaction mode is now available. You can get involved with the ChatDev team by playing the role of reviewer and making suggestions to the programmer. GitHub

DevGPT: Studying Developer-ChatGPT Conversations Yet, we know very little about how ChatGPT is actually used by software developers. What questions do developers present to ChatGPT? What are the dynamics of these interactions? What is the backdrop against which these conversations are held, and how do the conversations feedback into the artifacts of their work? To close this gap, the authors introduce DevGPT, a curated dataset which encompasses 17,913 prompts and ChatGPT’s responses including 11,751 code snippets, coupled with the corresponding software development artifacts—ranging from source code, commits, issues, pull requests, to discussions and Hacker News threads—to enable the analysis of the context and implications of these developer interactions with ChatGPT. Dataset

Releasing Persimmon-8B Permisimmon-8B is open-source, fully permissive model. It is trained from scratch using a context size of 16K. The model has 70k unused embeddings for multimodal extensions, and has sparse activations. The inference code combines the speed of C++ implementations (e.g. FasterTransformer) with the flexibility of naive Python inference. Hidden Size 4096 Heads 64 Layers 36 Batch Size 120 Sequence Length 16384 Training Iterations 375K Tokens Seen 737B Code and weights: https://github.com/persimmon-ai-labs/adept-inference

BioCoder: A Benchmark for Bioinformatics Code Generation with Contextual Pragmatic Knowledge BioCoder is a benchmark for code generation incorporating 2269 bioinformatics-specific coding problems. It incorporates a fuzz-testing framework for evaluation. The authors have applied BioCoder to evaluate many models including InCoder, CodeGen, CodeGen2, SantaCoder, StarCoder, StarCoder+, InstructCodeT5+, and ChatGPT. Dataset, benchmark, Docker images, and scripts: https://github.com/gersteinlab/biocoder

HumanEval_ru Dataset This is the Russian version of the code generation HumanEval dataset. Load dataset: from datasets import load_dataset load_dataset('NLPCoreTeam/humaneval_ru') DatasetDict({ train: Dataset({ features: ['task_id', 'prompt', 'canonical_solution', 'test', 'entry_point', 'signature', 'docstring', 'context', 'instruction', 'instruction_noexamples'], num_rows: 164 }) })

Understanding Llama 2 and Code Llama In this edition of the newsletter: the release of the Llama 2 base and chat models, as well as CodeLlama, the latest advances in the open source AI large language model landscape.

LLaMA-Reviewer: Advancing Code Review Automation with Large Language Models through Parameter-Efficient Fine-Tuning In this study, the authors present LLaMA-Reviewer, a framework that leverages LLaMA for automating code review. Two PEFT methods — zero-init attention prefix-tuning and LoRA tuning are used to address the computational challenge of LLM fine-tuning. RQs: 1. How effective is a LLM in automating code review tasks, compared to SoTA methods? 2. How does the representation of input data impact the performance of large language models? 3. How does instruction tuning influence the performance of subsequent sub-tasks? 4. What implications arise from different PEFT methods? Code, models, results: https://zenodo.org/record/7991113

Beating GPT-4 on HumanEval with a Fine-Tuned CodeLlama-34B CodeLlama-34B and CodeLlama-34B-Python were fine-tuned on an internal Phind dataset that achieved 67.6% and 69.5% pass@1 on HumanEval, respectively. GPT-4 achieved 67% according to their official technical report in March. The Phind models were trained over two epochs, for a total of ~160k examples. LoRA was not used — both models underwent a native fine-tuning. The authors employed DeepSpeed ZeRO 3 and Flash Attention 2 to train these models in three hours using 32 A100-80GB GPUs, with a sequence length of 4096 tokens. huggingface: - Phind/Phind-CodeLlama-34B-v1 - Phind/Phind-CodeLlama-34B-Python-v1

Consciousness in Artificial Intelligence: Insights from the Science of Consciousness The authors one of which is Yoshua Bengio derive a list of indicator properties from a survey of theories of consciousness. Each of these indicator properties is said to be necessary for consciousness by one or more theories, and some subsets are said to be jointly sufficient. The claim is that AI systems which possess more of the indicator properties are more likely to be conscious. It is discussed how AI systems could be constructed, or have been constructed, with each of the indicator properties. Also the authors consider whether some specific existing AI systems possess the indicator properties. These include Transformer-based LLMs, the Perceiver architecture, DeepMind’s Adaptive Agent and PaLM-E. This work does not suggest that any existing AI system is a strong candidate for consciousness.

Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models The results reveal the superiority and potential of PEFT over ICL (In-Context Learning) on a wide range of LLMs in reducing the computational burden and improving performance. Main results: - LLMs fine-tuned with PEFT techniques, i.e., a few millions of parameters, systematically outperform small language models fully fine-tuned with hundreds of millions of parameters - Prompt tuning often outperforms LoRA even though it requires learning substantially fewer parameters - LLMs fine-tuned using LoRA and Prompt tuning significantly outperform LLMs with ICL, even when increasing the number of prompt examples under the ICL setting - PEFT techniques allow LLMs to better adapt to the task-specific dataset with low computational cost

Introducing Code Llama, a state-of-the-art large language model for coding - Code Llama is a state-of-the-art LLM capable of generating code, and natural language about code, from both code and natural language prompts. - Code Llama is free for research and commercial use. - Code Llama is built on top of Llama 2 and is available in three models: - Code Llama, the foundational code model; - Code Llama - Python specialized for Python; - Code Llama - Instruct, which is fine-tuned for understanding natural language instructions. Github

https://arxiv.org/abs/2308.10825v1 Algebraic Topology for Data Scientists This book gives a thorough introduction to topological data analysis (TDA), the application of algebraic topology to data science. Algebraic topology is traditionally a very specialized field of math, and most mathematicians have never been exposed to it, let alone data scientists, computer scientists, and analysts. I have three goals in writing this book. The first is to bring people up to speed who are missing a lot of the necessary background. I will describe the topics in point-set topology, abstract algebra, and homology theory needed for a good understanding of TDA. The second is to explain TDA and some current applications and techniques. Finally, I would like to answer some questions about more advanced topics such as cohomology, homotopy, obstruction theory, and Steenrod squares, and what they can tell us about data. It is hoped that readers will acquire the tools to start to think about these topics and where they might fit in.

OWASP Top 10 for LLM The OWASP Top 10 for Large Language Model Applications project aims to educate developers, designers, architects, managers, and organizations about the potential security risks when deploying and managing Large Language Models (LLMs). The project provides a list of the top 10 most critical vulnerabilities often seen in LLM applications, highlighting their potential impact, ease of exploitation, and prevalence in real-world applications. Examples of vulnerabilities include prompt injections, data leakage, inadequate sandboxing, and unauthorized code execution, among others. The goal is to raise awareness of these vulnerabilities, suggest remediation strategies, and ultimately improve the security posture of LLM applications. 1 Prompt Injection 2 Insecure Output Handling 3 Training Data Poisoning 4 Model Denial of Service 5 Supply Chain Vulnerabilities 6 Sensitive Information Disclosure 7 Insecure Plugin Design 8 Excessive Agency 9 Overreliance 10 Model Theft PDF

A Survey of Time Series Anomaly Detection Methods in the AIOps Domain Internet-based services have seen remarkable success, generating vast amounts of monitored key performance indicators as univariate or multivariate time series. Monitoring and analyzing these time series are crucial for researchers, service operators, and on-call engineers to detect outliers or anomalies indicating service failures or significant events. Numerous advanced anomaly detection methods have emerged to address availability and performance issues. The review offers a comprehensive overview of time series anomaly detection in Artificial Intelligence for IT operations (AIOps), which uses AI capabilities to automate and optimize operational workflows. Additionally, it explores future directions for real-world and next-generation time-series anomaly detection based on recent advancements.

Knowledge Transfer from High-Resource to Low-Resource Programming Languages for Code LLMs The quality of code produced by a code LLM varies significantly by programming languages. The paper presents an effective approach for boosting the performance of code LLMs on low-resource languages using semi-synthetic data. Key ingredients: 1. The large volume of training data for high-resource programming languages includes a lot of well-documented code 2. Code LLMs are effective unit test generators, and we can check that generated tests pass 3. We can mechanically translate many unit tests to a low-resource language with a simple compiler 4. Code LLMs can translate code from one language to another, and we can test these translations with the aforementioned tests, and engineer a prompt to increase the likelihood of a successful translation The MultiPL-T datasets, and links to the fine-tuned models are available at huggingface.co/datasets/nuprl/MultiPL-T

notebook_whisperer A coding assistant to help with the construction of Jupyter notebooks. With the Notebook Whisperer, you can enter a short sentence saying what you would like to do. It then populates the next cell in your Jupyter notebook with the code for performing that task. This is accomplished by sending the contents of your notebook to chatGPT and having it provide the code that it thinks will fulfill your request.

PanGu-Coder2: Boosting Large Language Models for Code with Ranking Feedback In this paper, the authors introduce a novel framework, namely RRTF (Rank Responses to align Test&Teacher Feedback), and present a new Code LLM, namely PanGu-Coder2. Firstly, they adopt the Evol-Instruct technique to obtain a substantial amount of high-quality natural language instruction and code solution data pairs. Then, they train the base model by ranking candidate code solutions using feedback from test cases and heurstic preferences. Through comprehensive evaluations on HumanEval, CodeEval, and LeetCode benchmarks, PanGu-Coder2 achieves new state-of-the-art performance among billion-parameter-level Code LLMs, surpassing all of the existing ones by a large margin.

Towards Understanding the Capability of Large Language Models on Code Clone Detection: A Survey The study presented a comprehensive empirical evaluation of LLMs for automated code clone detection across diverse clone types, languages, and prompt formulations. The key findings demonstrate that advanced LLMs like GPT-3.5-Turbo and GPT-4 can achieve remarkably high recall and accuracy in detecting even complex semantic clones, outperforming existing techniques. Introducing intermediate reasoning steps through chain-of-thought prompting leads to noticeable gains by equipping models with a structured thought process. - Can LLMs detect code clones with a simple prompt? - How do LLMs perform by using one-step chain-of-thought prompts? - Can LLMs perform better by using multi-step chain-of-thought prompts? - How do LLMs perform using code embedding? - How does the performance of LLMs in code clone detection vary across different programming languages?