In-Context Learning
In-Context Learning

In-Context Learning

Have you ever encountered instances where ChatGPT repeatedly provides similar responses to your queries, or where its answers seem vague and unsatisfactory? Such issues often stem from the model's lack of awareness about your specific query data or its inability to deconstruct your request. Here are several strategies to help your Language Model (LM) deliver the results you intend:

In-context learning: This technique involves introducing a few-shot samples into the prompts, enabling the model to learn from real-world, in-context examples and generate more relevant responses. In-context learning is particularly effective for enhancing the model's reasoning abilities and logical deductions while keeping its core parameters unchanged.

Fine-tuning the language model: To improve the model's reasoning capabilities, you can leverage Chain-of-Thought (CoT) data to update its parameters. This process allows the LM to better handle complex problems and provide more accurate solutions.

?

Chain of Thought

Chain-of-Thought (CoT): CoT is a cutting-edge advancement aimed at enhancing LM's reasoning skills, especially for challenging tasks like solving mathematical or physical problems. CoT employs various strategies, including in-context learning and fine-tuning, to achieve this goal.

RAG Framework: The Retrieval-Augmented Generation (RAG) Framework enables the use of external knowledge bases to enhance the quality of LM responses. It leverages in-context learning and retrieval mechanisms to improve language modeling and provide natural source attribution.

?

RALM

RALM (Retrieval-Augmented Language Modeling): RALM involves selecting relevant documents from a knowledge corpus to condition a language model during text generation. This approach has proven highly effective in enhancing language modeling and ensuring accurate responses.

Langchain

Langchain: Langchain is a library framework that connects LLMs to external data sources. It empowers developers to chain together multiple commands, creating more complex applications. Azure has adopted this framework and released "prompt flow," a robust, production-ready version of Langchain that supports in-context learning and CoT for LLMs. It's important to note that Langchain is proprietary to OpenAI, so if you plan to work with other LLMs, you may need to explore alternative solutions.

I hope this information proves valuable to guide you on your journey with Language Models (LMs).

要查看或添加评论,请登录

Eeswar C.的更多文章

  • Retrieval Augumented Generation

    Retrieval Augumented Generation

    Anyone within the industry who has utilized ChatGPT for business purposes would likely have had the thought, "This is…

  • Diffusion Model - Gen AI

    Diffusion Model - Gen AI

    Diffusion models have gained attention for their ability to handle various tasks, particularly in the domains of image…

  • Anomaly Detection with VAE

    Anomaly Detection with VAE

    Anomaly detection is a machine learning technique used to identify patterns that are considered unusual or out of the…

  • Neural Network

    Neural Network

    In this article I am going back to the basics, Neural Networks! Most of the readers must have seen the picture above…

  • BERT - Who?

    BERT - Who?

    BERT - Bidirectional Encoder Representations from Transformers, isn’t that a tongue twister! 5 years ago, google…

  • How Does my Iphone know its me?

    How Does my Iphone know its me?

    Ever wondered how does iPhone know its you and never mistakes someone else for you when using Face Detection? Drum Roll…

    1 条评论
  • Natural Language Data Search

    Natural Language Data Search

    Remember how search was tedious a decade ago! Today you can search and ask questions in any search engine as you would…

  • Machine Learning & Data Privacy

    Machine Learning & Data Privacy

    Every person i know fears about how their personal data is at risk by all the AI/ML that is surrounding them, whether…

  • Business at center of Data Science

    Business at center of Data Science

    Any one who has participated in brainstroming & whiteboarding sessions would agree that, what data scientists think of…

  • Capsule Networks (#capsnets)

    Capsule Networks (#capsnets)

    In my previous article on Handwriting Decoder (#ocr), we touched on how can we read Hand Writing using Computer vision.…

社区洞察

其他会员也浏览了