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awesome-generative-ai-guide

A one stop repository for generative AI research updates, interview resources, notebooks and much more!
https://github.com/aishwaryanr/awesome-generative-ai-guide

Last synced: 22 days ago
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:mortar_board: Courses

:notebook: Code Notebooks

:paperclip: Resources

🌟🌟 Repo of the Month!

:star: Best GenAI Papers List (April 2024)

  • Octopus v4: Graph of language models - source language models optimized for specific tasks. Octopus v4 excels in directing user queries to the most appropriate model and reformulating queries for optimal performance, building upon previous iterations (v1, v2, and v3) with enhanced selection and parameter understanding. Additionally, it explores the use of graphs as a versatile data structure to coordinate multiple models effectively. | Foundational LLM |
  • Better & Faster Large Language Models via Multi-token Prediction - token prediction on generative benchmarks like coding, showing notable gains in problem-solving tasks. Moreover, models trained with multi-token prediction demonstrate up to threefold faster inference speeds, even with large batch sizes, offering additional efficiency benefits. | New Architecture |
  • Extending Llama-3's Context Ten-Fold Overnight - 3-8B-Instruct model's context length is extended from 8K to 80K through efficient QLoRA fine-tuning, requiring only 8 hours on a single 8xA800 GPU machine. This extension significantly enhances model performance across various evaluation tasks like NIHS and topic retrieval, while maintaining proficiency in short-context tasks. Surprisingly, the extension is achieved with just 3.5K synthetic training samples from GPT-4, showcasing the untapped potential of LLMs to extend context lengths. The team plans to release all associated resources publicly, including data, model, data generation pipeline, and training code. | Context Length |
  • Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Models - The PoLL approach, composed of a larger number of smaller models, outperforms single large judges across three distinct settings and six datasets. It exhibits less intra-model bias and is over seven times less expensive, offering a cost-effective and more reliable evaluation method for LLMs. | Evaluation |
  • AdvPrompter: Fast Adaptive Adversarial Prompting for LLMs - readable adversarial prompts, called AdvPrompter, to address jailbreaking attacks on LLMs. Unlike existing optimization-based approaches, AdvPrompter achieves adversarial prompt generation in seconds, 800 times faster, without requiring access to gradients from the TargetLLM. The method alternates between generating high-quality target adversarial suffixes and low-rank fine-tuning of AdvPrompter. Experimental results demonstrate state-of-the-art performance on the AdvBench dataset and transferability to closed-source black-box LLM APIs. | Adversarial Attacks, Evaluation |
  • Capabilities of Gemini Models in Medicine - Gemini, a specialized multimodal model for medical tasks, surpasses GPT-4 on various benchmarks, achieving state-of-the-art results in medical text summarization and question answering. With its advanced long-context reasoning, it outperforms existing methods in tasks such as needle-in-a-haystack retrieval from medical records. While promising, further evaluation is needed before deployment in real-world medical applications. | Domain-Specific LLMs |
  • How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites - source multimodal large language model (MLLM), bridges the gap between open-source and proprietary commercial models in multimodal understanding. It introduces three improvements: a Strong Vision Encoder, Dynamic High-Resolution image processing supporting up to 4K resolution, and a High-Quality Bilingual Dataset. Evaluation across benchmarks demonstrates its effectiveness compared to both open-source and proprietary models. | Multimodal LLMs |
  • Make Your LLM Fully Utilize the Context - intensive (IN2) training to address the lost-in-the-middle challenge faced by contemporary LLMs. Leveraging a synthesized long-context question-answer dataset, IN2 training emphasizes fine-grained information awareness within long contexts. Applying this approach to Mistral-7B yields FILM-7B (FILl-in-the-Middle), which robustly retrieves information from various positions in a 32K context window. FILM-7B improves performance on real-world long-context tasks while maintaining comparable performance on short-context tasks | Context Length |
  • SEED-Bench-2-Plus: Benchmarking Multimodal Large Language Models with Text-Rich Visual Comprehension - Bench-2-Plus, a benchmark specifically tailored for evaluating text-rich visual comprehension of Multimodal Large Language Models (MLLMs). With 2.3K multiple-choice questions covering Charts, Maps, and Webs, it aims to simulate real-world text-rich scenarios comprehensively. Evaluation involving 34 prominent MLLMs highlights current limitations in text-rich visual comprehension, emphasizing the need for further research and improvement in this area. SEED-Bench-2-Plus serves as a valuable addition to existing MLLM benchmarks, offering insightful observations and inspiring future developments in text-rich visual comprehension. | Multimodal LLMs |