Results of the summarizing process for the arXiv paper: 2311.01460v1

In the paper "Implicit Chain of Thought Reasoning via Knowledge Distillation," authors Yuntian Deng, Kiran Prasad, Roland Fernandez, Paul Smolensky, Vishrav Chaudhary, and Stuart Shieber explore an alternative approach to reasoning in language models (LMs). Rather than prompting or finetuning LMs to generate a chain of thought reasoning steps before producing the final answer as is typically done by researchers, this study suggests that LMs could reason more effectively with intermediate computation that is not expressed in natural language. The authors propose using the internal hidden states of the language model to perform implicit reasoning instead of explicitly producing the chain of thought reasoning steps. They distill these implicit reasoning steps from a teacher model trained on explicit chain-of-thought reasoning. This approach enables vertical reasoning among the hidden states in different layers rather than conducting horizontal reasoning by generating intermediate words one-by-one. To evaluate their approach, the researchers conduct experiments on a multi-digit multiplication task and a grade school math problem dataset. The results demonstrate that this alternative reasoning method allows for solving tasks that were previously unsolvable without explicit chain-of-thought reasoning while achieving comparable speed to no chain-of-thought reasoning. Overall, this study presents a novel way to enhance language models' ability to reason by leveraging implicit reasoning through hidden states. By distilling knowledge from an explicit chain-of-thought teacher model, the proposed approach opens up new possibilities for solving complex tasks effectively and efficiently.

• Authors propose an alternative approach to reasoning in language models (LMs)
• LMs can reason effectively with intermediate computation not expressed in natural language
• Internal hidden states of the language model used for implicit reasoning instead of explicit chain-of-thought reasoning steps
• Implicit reasoning steps distilled from a teacher model trained on explicit chain-of-thought reasoning
• Vertical reasoning among hidden states in different layers instead of horizontal reasoning by generating intermediate words
• Experiments conducted on multi-digit multiplication task and grade school math problem dataset
• Results show that this alternative reasoning method allows solving previously unsolvable tasks without explicit chain-of-thought reasoning
• Comparable speed achieved to no chain-of-thought reasoning
• Enhances language models' ability to reason by leveraging implicit reasoning through hidden states
• Opens up new possibilities for solving complex tasks effectively and efficiently.

Authors propose a different way for language models to think and solve problems. Language models can use hidden information inside them to think, instead of just using words. They learn how to think by studying a teacher model that thinks step by step. Instead of thinking horizontally by saying words, they think vertically by using different layers of hidden information. They tested this new way of thinking on math problems and it worked really well. It helps language models solve hard problems without needing to say every thought out loud. This new way of thinking makes language models smarter and faster at solving difficult tasks." Definitions- Reasoning: The process of thinking and figuring things out. - Language models: Computer programs that understand and generate human language. - Hidden states: Information that is stored inside something but not easily seen or heard. - Implicit reasoning: Thinking without saying every thought out loud. - Chain-of-thought reasoning: Thinking step by step in a clear order. - Vertical reasoning: Thinking using different levels or layers of information. - Experiments: Tests or trials done to see if something works or not. - Dataset: A collection of data used for studying or testing something. - Enhances: Makes better or improves something.

Exploring Implicit Chain of Thought Reasoning via Knowledge Distillation

In recent years, language models (LMs) have become increasingly powerful and capable of solving complex tasks. However, most approaches to reasoning in LMs rely on prompting or finetuning them to generate a chain of thought reasoning steps before producing the final answer. In their paper "Implicit Chain of Thought Reasoning via Knowledge Distillation," Yuntian Deng, Kiran Prasad, Roland Fernandez, Paul Smolensky, Vishrav Chaudhary, and Stuart Shieber propose an alternative approach that leverages implicit reasoning through hidden states rather than explicit chain-of-thought reasoning. This study demonstrates that this method can enable LMs to solve tasks that were previously unsolvable without explicit chain-of-thought reasoning while achieving comparable speed to no chain-of-thought reasoning.

Background

The authors note that traditional approaches for enabling LMs to reason involve prompting or finetuning them with explicit chains of thought prior to generating the final answer. While these methods are effective in some cases, they require significant computational resources and may not be suitable for more complex tasks due to the difficulty in constructing such prompts or finetuning parameters. The authors suggest an alternative approach which leverages implicit knowledge from hidden states within the LM instead of explicitly producing intermediate words one by one as is typically done by researchers.

Proposed Methodology

To implement this approach, the authors propose using knowledge distillation from a teacher model trained on explicit chain-of-thought reasoning steps. This allows for vertical reasoning among different layers within the LM rather than conducting horizontal reasoning by generating intermediate words one at a time as is typically done with other methods. To evaluate their proposed method, they conduct experiments on two datasets: a multi-digit multiplication task and a grade school math problem dataset.

Experimental Results

The results demonstrate that this alternative method enables LMs to solve tasks which were previously unsolvable without explicit chain-of-thought reasoning while achieving comparable speed compared with no chain-of -thought reasoning at all. Furthermore, it was found that when combined with prompt generation techniques such as reinforcement learning or evolutionary strategies ,the proposed approach can further improve performance on certain types of problems .

Conclusion

Overall ,this study presents a novel way to enhance language models' ability to reason by leveraging implicit knowledge from hidden states through knowledge distillation from an explicit teacher model trained on chains of thought . By doing so ,this opens up new possibilities for solving complex tasks effectively and efficiently .