Introduction:
Language models are undoubtedly powerful and capable of performing a wide range of tasks. However, when faced with complex questions, they can struggle, as their primary function is to predict and generate token completions. In this blog, we'll explore how the concept of "Chain of Thoughts" and "Program-Aided Language Models (PAL)" can empower LLMs to tackle intricate challenges more effectively.
Problem:
We often turn to Large Language Models (LLMs) for their remarkable abilities, but it's essential to acknowledge their limitations. LLMs excel at generating token completions, making them invaluable for various applications. However, when it comes to handling complex tasks or questions, they can stumble. For example, consider the below example, the correct output should be 9 but the LLM provides us 27.
Chain of thought(COT):
To address this limitation, we can employ a concept known as the "Chain of Thoughts." This approach involves breaking down a problem into a series of interconnected steps, mirroring how a human would reason through it. The Chain of Thoughts technique leverages the LLM's predictive abilities, guiding it along a path of reasoning toward the desired answer.
Chain of Thoughts is a powerful tool, but even it has its boundaries. LLMs may struggle with tasks that require complex arithmetic operations, as these models aren't inherently designed for such computations. This is where "Program-Aided Language Models" (PAL) come into play.
Program Aided Language Models (PAL):
PAL is an augmentation of LLMs that combines them with a code interpreter capable of handling complex arithmetic tasks.
The PAL approach involves providing the LLM with examples in the form of a "prompt template." This template includes a broken-down example, the corresponding code for each step, and a user question. The LLM, armed with this PAL prompt template, converts the arithmetic portion into executable code, which is then passed to the code interpreter.
The result generated by the code interpreter is seamlessly integrated into the prompt template, where the user's question is addressed. This entire orchestration, from code interpretation to user response, is facilitated by an orchestrator library.
Conclusion:
In essence, Chain of Thoughts and PAL represent two valuable strategies for enhancing LLM capabilities, enabling them to tackle intricate problems and perform complex arithmetic operations efficiently. With these tools, LLMs can expand their utility in various domains and become even more versatile problem solvers.