Implicit Chain-of-Thought: Internalizing Reasoning in Language Models

Yuntian Deng

University of Waterloo

The NLP Reading Group is delighted to host Prof. Yuntian Deng who will be giving a talk remotely on “Implicit Chain-of-Thought: Internalizing Reasoning in Language Models”.

Recording

The recording for the talk can be found here on our YouTube channel.

Talk Description

When leveraging language models for reasoning tasks, generating explicit chain-of-thought (CoT) steps is often crucial for high accuracy. In this work, drawing inspiration from how the human brain transitions from explicit, conscious, deliberate reasoning (System 2) to implicit, automatic, intuitive thinking (System 1), we seek to internalize explicit CoT reasoning within a model that directly produces the final answer, which we define as the implicit CoT paradigm.

To realize implicit CoT, we found a simple yet effective method: starting with a model trained for explicit CoT reasoning, we gradually remove the intermediate steps and finetune the model. This approach enables a finetuned GPT-2 Small model to solve 20-by-20 multiplication with up to 99.5% accuracy, whereas standard training cannot solve beyond 4-by-4 multiplication.

You can try our demo at https://huggingface.co/spaces/yuntian-deng/gpt2-multiplication

Speaker Bio

Yuntian Deng is an assistant professor at the University of Waterloo and a visiting professor at NVIDIA under Prof. Yejin Choi. He was previously a postdoc at AI2, also advised by Prof. Choi. He received his PhD from Harvard University under Prof. Alexander Rush and Prof. Stuart Shieber. His recent works include NeuralOS, Interactive Training, WildChat, and Implicit Chain-of-Thought.

Logistics

Date: October 17th
Time: 2:00PM
Location: H04 or via Google Meet (See email)