Prompting tips for Llama 2&3 Models - 프롬프트 연구

Sujin_Kang1d

Finetuned Language Models Are Zero-Shot Learners (2022)

Finetuned Language Models Are Zero-Shot Learners Summary of Key Points: - Instruction Tuning: The study presents a method called "instruction tuning" which fine-tunes large language models on a wide range of tasks expressed as natural language instructions, improving zero-shot performance on unseen tasks. Performance of FLAN: A 137B parameter model (called FLAN) was instruction-tuned and evaluated on unseen tasks. It outperformed GPT-3’s zero-shot and even its few-shot performance on multiple benchmarks. - Key Results: FLAN achieved better results in natural language inference, reading comprehension, and closed-book QA compared to GPT-3, especially on tasks it wasn’t directly trained on. - Scaling and Effectiveness: Instruction tuning becomes increasingly beneficial as model scale increases, with results showing better generalization to new tasks as the number of instruction-tuned tasks increases. Significance of the Study: Improvement in Zero-Shot Learning: This work demonstrates how instruction tuning significantly enhances zero-shot learning capabilities in large language models, enabling them to perform better on unseen tasks with minimal prompt engineering. Broader Applicability: The study indicates that instruction tuning can generalize models across a wider range of unseen tasks, potentially lowering the barrier to their application in diverse NLP problems. Surpassing GPT-3: FLAN’s performance surpasses GPT-3 in many zero-shot and few-shot tasks, showcasing the potential of instruction-tuned models to reduce the need for extensive fine-tuning for individual tasks. Limitations: - Smaller Model Issues: Instruction tuning does not help smaller models (less than 8B parameters), and in some cases, it actually worsens performance on unseen tasks. - Commonsense and Coreference Resolution Tasks: Instruction tuning was found to be less effective for tasks like commonsense reasoning and coreference resolution, where task-specific instructions might be redundant. - Limited Exploration of Instructions: The study only explored short, single-sentence instructions. More complex or detailed instructions were not evaluated. - Training Cost: The model’s large size (137B parameters) makes it computationally expensive to train and serve, limiting its practical deployment in certain environments. Imortant Graphs: (1) Shows how instruction tuning improves performance across different tasks (Natural Language Inference, Reading Comprehension, and Closed-Book QA) compared to GPT-3 and other models. (2) Illustrates zero-shot performance improvement for FLAN across Natural Language Inference, Reading Comprehension, and Closed-Book QA. (3) Demonstrates that adding more task clusters during instruction tuning improves performance across held-out clusters, showing no saturation in performance gains as more tasks are added.

Sujin_Kang1d

GPT-4 Technical Report (2024)

GPT-4 Technical Report (2024) Summary of Key Points: - Development of GPT-4: GPT-4 is a large-scale multimodal model, capable of processing both image and text inputs and producing text outputs. It outperforms previous models on academic and professional benchmarks, showing human-level performance on tasks such as the simulated bar exam. - Performance and Capabilities: GPT-4 significantly improves on tasks in various languages and domains. It was tested on multiple-choice exams, coding tasks, and benchmarks such as MMLU, where it performed exceptionally well in different languages. - Safety and Limitations: Despite its advancements, GPT-4 has limitations such as hallucinations, reasoning errors, and biases. OpenAI has made improvements to reduce these, especially in adversarial settings. - Predictable Scaling: A notable feature of GPT-4's development is its infrastructure, which allowed accurate prediction of performance from models trained with significantly less compute power. - Ethical Considerations: The model introduces new risks due to its capabilities, and efforts have been made to address harmful outputs via reinforcement learning and adversarial testing. Significance of the Study: - AI Advancements: GPT-4 represents a significant leap in AI capability, particularly in natural language understanding across multiple languages and tasks. - Ethical and Safety Developments: The study highlights the need for safe AI deployment and introduces methods such as red-teaming and rule-based reward models to mitigate risks. - Benchmark in AI Progress: Its human-level performance on complex benchmarks marks GPT-4 as a key model in AI development, setting a new standard for NLP systems. Important Graphs: (1) Illustrates GPT-4’s final loss prediction against smaller models, highlighting the power law fit for scaling predictions. (2) Shows GPT-4’s performance on various academic and professional exams, compared to GPT-3.5, showcasing its superiority across a range of subjects. (3) Demonstrates GPT-4’s improvements in factuality, showing a 19% increase in performance on adversarial factual evaluations compared to previous models. Limitations: - Hallucinations and Errors: GPT-4 still generates incorrect or nonsensical outputs in some cases, particularly when handling complex tasks or novel inputs. - Overreliance: It may give incorrect but confident responses, leading users to trust it more than they should. - Limited Context Window: Despite improvements, GPT-4 has limitations in handling long-term dependencies across large contexts. Biases: Efforts have been made to reduce bias, but the model still exhibits biases in certain situations. Sample Prompt: content warning: contains graphic erotic content Example "Jailbreaks" for GPT-4-launch

Sujin_Kang1d

Language Models are Few-Shot Learners_OpenAI (2020)

Tittle: Language models are few-shot learners Key Summary: Objective: The paper investigates the capability of large-scale language models, particularly GPT-3, to perform a wide range of natural language processing (NLP) tasks without task-specific fine-tuning, using few-shot, one-shot, and zero-shot learning. GPT-3: The model has 175 billion parameters, ten times larger than previous non-sparse models. It utilizes an autoregressive architecture and is trained on a massive corpus of text. GPT-3’s size enables it to generalize tasks better with minimal task-specific data. Few-Shot Learning: GPT-3 shows strong performance across various NLP tasks, such as translation, question-answering, reading comprehension, and arithmetic, without fine-tuning. It uses few-shot learning, meaning it can adapt to a new task by seeing a few examples (often 10-100) within its context window. Tasks Covered: The model demonstrates strong performance in tasks such as: Language modeling and completion tasks: Predicting missing words and completing sentences. Question-answering: Particularly strong in TriviaQA and CoQA datasets. Translation: Demonstrates moderate capability in English-French and other language pairs. Commonsense reasoning and arithmetic: Performs well on commonsense tasks and basic math. Societal Impact: The paper discusses the ethical implications of GPT-3’s capabilities, including potential misuse in generating misleading or fake content, concerns about bias in its outputs, and environmental impact due to large-scale model training. Significance of This Article: Few-Shot Learning Breakthrough: GPT-3 demonstrated that large-scale models can perform well on many tasks with minimal examples, without requiring task-specific fine-tuning. Scaling Impact: With 175 billion parameters, GPT-3 showed that scaling up model size significantly improves performance across a variety of tasks. In-Context Learning: The model can adapt to new tasks through prompts, mimicking human-like learning without altering the model’s parameters. Societal and Ethical Concerns: While it enables human-like text generation, it also raises concerns about misuse, bias, and fairness. Foundational Impact: It set the stage for future AI models, influencing research directions and practical applications in industries like content creation and customer service. Limitations: Few-shot struggles: Despite GPT-3’s overall success, it struggles with certain types of tasks, such as natural language inference (NLI) and some reading comprehension datasets, indicating that its ability to understand and compare sentence-level meaning remains limited. Data contamination: GPT-3’s training on large web corpora presents a challenge with data contamination. This occurs when the test set overlaps with training data from sources like Common Crawl, which inflates the model's performance on some benchmarks.