Large Language Models (LLMs) have shown impressive capabilities in language understanding and reasoning. To enhance their proficiency, researchers have employed the chain of thought (CoT) technique but it delays the generation of the desired answer. In this paper, the authors propose an implicit CoT reasoning approach that allows the model to produce the final answer directly by internalizing the intermediate steps during training. The method was tested on math problems and proved effective, though it has some limitations.
Can Language Models Reason Beyond Words? Exploring Implicit Reasoning in Multi-Layer Hidden States for Complex Tasks
Large Language Models (LLMs) have revolutionized the way we interact with AI systems by enhancing language understanding and reasoning capabilities. However, researchers have discovered a new approach called implicit chain-of-thought reasoning that further improves the proficiency of LLMs.
The Problem with Chain of Thought Reasoning
While chain of thought (CoT) reasoning methods have shown great results, they often result in a delay in generating the final answer. To overcome this limitation, researchers have developed an implicit CoT reasoning approach. This approach makes the reasoning steps implicit, allowing the model to produce the final answer directly.
How Implicit CoT Reasoning Works
In implicit CoT reasoning, the model is trained to see the intermediate steps only during the training phase, not during testing. It processes these steps in its internal states and learns to internalize the concept thoroughly, bypassing explicit reasoning.
The researchers used a ‘teacher training’ method to achieve implicit CoT reasoning. First, they trained a student model to read the teacher’s hidden states and utilize some of them to produce the final answer. Then, they employed knowledge distillation to transfer knowledge from a larger model to a smaller one. They trained an emulator to predict the teacher’s hidden states based on input. This emulation happens vertically across the model’s layers, eliminating the need for explicit reasoning steps.
The final step involves combining the emulator with the student model, which produces the final output based on the emulated teacher’s thought process. The integrated system is optimized end-to-end, enabling the student model to develop its own reasoning methods.
Results and Benefits
The researchers conducted experiments on multi-digit multiplication and grade school math problems. Their method equipped the models to solve previously unsolvable tasks without explicit CoT. The implicit CoT technique also showed higher inference speed, especially for tasks that require multiple intermediate steps.
Limitations and Future Improvement
Some limitations of this technique include the lack of transparency, heavy dependence on the teacher’s thought processes, and lagging in performance compared to explicit CoT. However, this work is just an initial step, and the researchers believe that further adjustments can optimize the implicit CoT process and enhance LLMs’ ability to reason.
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