Human in The Loop

Author(s): Lalit Kumar

Originally published on Towards AI.

Enhancing LLM output

Last week, during our routine Tea break, my friend and I started discussing about GenAI and the ongoing concern on whether AI will replace humans(us). Are the current LLMs taking jobs or reducing dependency on humans? While such questions are valid up to some extent, the current limitations of LLMs on the other hand require a lot of help from human reviewers to make the LLM output relevant.

In this article, I will try to uncover various aspects of LLM development where human feedback and involvement is critical.

Large Language Models (LLMs) are at the centre stage of the technology world with models like OpenAI’s ChatGPT, Google’s Gemini, Anthropic’s Claude, and Meta’s Llama. These models are trained on almost all the data available on Internet including vast amounts of text and code. These models demonstrate amazing abilities in generating human-like text, translating languages, generating creative content and answering questions in a very structured way. They act as custom chatbots, programmer assistants, create marketing content and are finding applications in almost all the fields.

However, despite their sophistication, LLMs do have limitations, they do tend to make mistakes(https://medium.com/@lalit.k.pal/common-mistakes-in-using-genai-and-how-to-counter-them-b2f9a194395d?sk=62feea94291cfcab63b4bf623dd5de27) like hallucinations, inherit and amplify biases present in their training data, struggle with nuances and common-sense… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI