Cappy: The Small Scorer Making Big Waves in Language Models

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Get ready to meet Cappy, the small but mighty scorer that's taking the world of multi-task language models by storm. Developed by Google Research, Cappy is proving that size isn't everything when it comes to boosting the performance and efficiency of large language models (LLMs).

Key Points:

1. Cappy is a lightweight pre-trained scorer with only 360 million parameters, based on continual pre-training on top of RoBERTa.

2. It takes an instruction and a candidate response as input, producing a score between 0 and 1 that indicates the estimated correctness of the response.

3. Cappy can function independently on classification tasks or serve as an auxiliary component for LLMs, enhancing their performance.

4. Adapting LLMs with Cappy is efficient and doesn't require access to LLM parameters, making it compatible with closed-source multi-task LLMs.

The Rise of Multi-Task LLMs

Multi-task LLMs, like T0, FLAN, and OPT-IML, have been making waves in the AI world. They're trained on instruction-response pairs, allowing them to address unseen tasks by understanding and solving brand-new instructions. But there's a catch: these models are massive, ranging from several billion to hundreds of billions of parameters. That means they need a lot of computational power and memory, making them expensive and inefficient to train and use.

Enter Cappy, the small scorer with big potential.

How Cappy Works

Cappy is pre-trained on a diverse dataset of instruction-response pairs, each with a correctness annotation ranging from 0 to 1. This annotation is generated using Rouge-L, a metric that measures the similarity between a response and the ground truth response.

When it's time to solve a task, Cappy uses a candidate-selection mechanism. Given an instruction and a set of candidate responses, Cappy scores each response and selects the one with the highest score as its prediction. For classification tasks, Cappy can work independently. But for generation tasks, it teams up with an existing multi-task LLM to create the candidate responses.

Adapting LLMs with Cappy

One of the coolest things about Cappy is how it can adapt LLMs to downstream tasks when there's training data available. By fine-tuning Cappy on a task-specific regression dataset, it can integrate that information into the LLM's predictions, boosting performance.

What's more, adapting LLMs with Cappy is way more efficient than other tuning strategies. It doesn't need to do back-propagation through LLM parameters, so it uses less memory. And since it doesn't need access to those parameters, it can work with closed-source LLMs that are only accessible via WebAPIs.

Cappy's Impressive Results

So, how does Cappy stack up against the big boys? Pretty darn well, it turns out. In tests on eleven held-out language understanding classification tasks, Cappy outperformed OPT-175B and OPT-IML-30B, and matched the accuracy of T0-11B and OPT-IML-175B. Not bad for a little scorer with just 360M parameters!

Cappy also shone when adapting LLMs to complex tasks from BIG-Bench. It consistently enhanced the performance of FLAN-T5 models, beating out the best baseline achieved through self-scoring.

The Future of Cappy

While the experiments so far have focused on adapting a single LLM to multiple domains, the researchers behind Cappy see even more potential for their pre-trained model. They envision it being used in creative ways beyond just single LLMs.

So, what do you think about Cappy? Are you excited to see how this small scorer could change the game for multi-task language models? Let us know in the comments!

And if you want to dive deeper into the world of AI and language models, check out these related articles:

• The Rise of Multi-Task Language Models: A Game-Changer for AI

• Efficient Language Model Adaptation: Techniques and Strategies

• The Future of Natural Language Processing: Trends and Predictions

Source: https://blog.research.google/2024/03/cappy-outperforming-and-boosting-large.html

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