Demystifying Few-Shot Learning: Enhancing Base LLM for text summarization: Part 1
Abhijeet Ambekar
MLOps Engineer @ Home Depot | Former Reinforcement Learning | Published ML & AR Research with 100+ Citations | Elevating ML & Generative AI through Scalable Solutions ??
Imagine trying to summarize an entire conversation in a few sentences. Tricky, right? ?? That's the challenge AI models face in text summarization tasks. But what if we could fine-tune them for a particular task? That's exactly what we'll explore in this journey of tweaking Base LLMS (Large Language Models) to ace text summarization. In a nutshell, Base LLMs understand human language but are not necessarily good at particular tasks. Let's check how can we overcome this issue. ???
?? Problem Statement
Base LLMS are great at understanding the human language, but when performing a particular task, they need some hand-holding. They can churn out summaries that are more confusing than the dialogue itself! ?? For professionals across fields, from software engineering to marketing, getting crisp, clear summaries is essential. We need AI that doesn't just replicate human abilities but enhances them. That's where the real challenge lies. How do we channel the abilities of the model for a particular task?
1. The Basic Approach - base model inference
First, let's dive into the world of LLM with the FLAN-T5 model from Hugging Face, a popular model for language tasks. We used this model to summarize a piece of dialogue but found that the output was, well, underwhelming. The model seemed to struggle to capture the essence of the conversation.
Here is the output of the above inference compared to human baseline summary.
2. Prompt Engineering
Now let's try a technique called 'prompt engineering'. It's like giving the model a nudge in the right direction. We used two different prompts: a generic instruction and a FLAN-T5 template. Sadly, even with these prompts, our model couldn't produce a summary that made us go "Wow!"
Let's check how the output of the above prompts compares to human baseline.
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3. Few-Shot Learning to the Rescue
Finally, let's experiment with few-shot learning. It's a bit like giving the model a few examples to learn from. And guess what? This approach significantly improves the summarization quality! It turns out, after a few examples, the AI gets the hang of it.
Let's compare the output of a few-shot prompt with the previous two techniques.
?? Conclusion
So, what have we learned? Few-shot learning is like giving the LLM model a cheat sheet, and it works wonders! ?? By using a few examples, we can train the AI model to produce summaries that are not just accurate but also meaningful. And the best part? You don't need to be a mathematician or an AI guru to apply these techniques. Stay tuned for more AI adventures where we'll dive into model fine-tuning. Imagine the possibilities when we unlock the full potential of LLMs!
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Data Scientist @ Keck School of Medicine USC | AI for Mental Health
1 年Very cool! I see some potential applications in psychotherapy, where conversations last for at least 40 minutes. Being able to generate a concise summary will help supervisors evaluate clinicians-in-training and also provide practicing clinicians with another source of data apart from their notes without the need to go through the entire transcript.