Meta has announced the launch of Purple Llama, an umbrella project promoting open trust and safety in generative AI. The project features tools and evaluations designed to enable developers to deploy generative AI models and experiences responsibly in line with best practices outlined in Meta's Responsible Use Guide. The first components of Purple Llama include CyberSec Eval, a set of cybersecurity safety evaluation benchmarks for large language models (LLMs), and Llama Guard, a safety classifier for input/output filtering optimized for easy deployment.
The release of these tools aligns with Meta's open approach. The company intends to collaborate with various industry leaders and organizations, including the recently announced AI Alliance, AMD, AWS, Google Cloud, Hugging Face, IBM, Intel, Lightning AI, Microsoft, MLCommons, NVIDIA, Scale AI, and more, to enhance and make these tools available to the open-source community.
Generative AI has ushered in a new era of innovation, allowing for the creation of conversational AI, realistic imagery generation, and effective summarization of extensive document corpora. Meta, with over 100 million downloads of Llama models, recognizes the importance of collaboration on safety to build trust among developers driving this innovation. The goal is to collectively address AI challenges, fostering responsible AI development through an open and collaborative approach.
Purple Llama, as an umbrella project, aims to bring together various tools and evaluations over time to assist the community in building responsibly with open generative AI models. The initial release includes tools and assessments focused on cybersecurity and input/output safeguards, with additional tools planned for the future.
The first step in this initiative involves addressing key considerations in generative AI safety, focusing on cybersecurity. Meta shares what it believes is the first industry-wide set of cybersecurity safety evaluations for LLMs based on industry guidance and standards. These benchmarks aim to provide metrics for quantifying LLM cybersecurity risks, tools to evaluate the frequency of insecure code suggestions, and tools to make generating malicious code or carrying out cyberattacks more challenging.
Additionally, Meta is releasing Llama Guard, an openly available model designed to compete effectively on standard open benchmarks. Llama Guard serves as a pre-trained model to help developers defend against generating potentially risky outputs by checking and filtering inputs and outputs in line with content guidelines.
Meta is releasing the methodology and an extended discussion of model performance in its Llama Guard paper to emphasize its commitment to open and transparent science. The company envisions allowing developers to customize this model to support relevant use cases and make adopting best practices within the open ecosystem easier.
The name "Purple Llama" reflects the comprehensive approach the project intends to take in mitigating challenges posed by generative AI. Purple teaming, which involves red and blue team responsibilities, is a collaborative approach to evaluating and mitigating potential risks, and Meta intends to apply this ethos to generative AI.
Meta's investment in Purple Llama reinforces the company's commitment to fostering an open ecosystem in AI. This collaborative mindset was evident when Llama 2, featuring over 100 partners, was launched in July. Many of these partners will continue to collaborate with Meta on open trust and safety, and the company is excited to engage with others who share the vision of a responsibly developed, generative AI ecosystem.
Meta is set to host a workshop at NeurIPS 2023, where it plans to share the tools and provide a technical deep dive to help individuals get started.
In the ever-evolving programming world, language barriers often act as obstacles for developers, especially those inexperienced in English. The dominance of English in technical documentation challenges non-native speakers, hindering their ability to access valuable information. AutoTranslateDoc is a command-line tool designed to democratize access to technical documentation by breaking down language barriers.
AutoTranslateDoc employs a systematic process to bridge the language gap in programming:
Collect the Documentation: The tool connects to GitHub, identifying and downloading .md and .mdx files from any repository.
Chunk and Prepare: Documentation is chunked or split for translation, making the process more manageable.
Translate Efficiently: Leveraging the power of advanced language models like GPT-3.5 and GPT-4, each chunk of documentation is accurately translated.
Verify and Enhance: Translations are automatically verified, with retranslation if needed, ensuring the highest quality.
Consolidate: The translated chunks are then amalgamated back into a cohesive document.
Initial tests on translating llamaIndexTS documentation have been highly promising, enabling users to read the documentation in over a dozen languages, including Chinese, French, and Spanish.
AutoTranslateDoc places a strong emphasis on improving translation accuracy through innovative approaches:
Strategic Document Splitting: Each documentation page is divided into sections, maintaining the original structure and thematic relevance during translation.
Rigorous Translation Verification: Several checks, including translation length, title hierarchy analysis, link count validation, and code block accuracy, are performed to ensure the accuracy and consistency of translations.
This dual approach ensures that translations are accurate and contextually relevant, maintaining the integrity and utility of the original documentation. The tool even incorporates a unique self-critique feature, where the language model evaluates its translation output, further refining quality.
Recognizing the dynamic nature of documentation, AutoTranslateDoc integrates a robust system to manage updates efficiently:
Historical Tracking through JSON: A .json file is generated during translation, chronicling the history of translations. This file supports differential translation, identifying and translating only newly added or modified content.
AutoTranslateDoc is actively working on additional features to streamline the translation and update processes further:
Manual Change Integration: Acknowledging that translations might undergo manual edits post-generation, the tool develops functionality to account for these changes during updates.
GUI for Translation Management: In the early stages of development, a graphical user interface (GUI) aims to simplify the process of translation editing, tracking, and verification. This feature will allow users to interact more intuitively with translations.
By continually refining these features, AutoTranslateDoc aims to stay at the forefront of making technical documentation accessible and easy to maintain in multiple languages.