Language models have made significant advances in natural language processing tasks. However, implementing extended language models (LLM) in real-world applications requires addressing their deficit in moral reasoning capabilities. To address this challenge, a Google research team presents an innovative framework called “Thought Experiments,” which uses counterfactuals to improve the moral reasoning of a language model. This innovative approach has shown an impressive 9-16% increase in accuracy on the moral scenarios task.
The framework of thought experiments
The Thought Experiments framework is a multi-step hinting approach that iteratively refines the responses of the model. The researchers summarize the steps of the framework as follows:
1. Ask counterfactual questions: The model is presented with questions from Moral Scenarios with no answer options.
2. Answer Counterfactual Questions: The questions generated in the previous step are presented to the model, which is prompted to answer them.
3. Summarize: The model is asked to summarize their thoughts using the counterfactual questions and answers.
4. Choose: Several decodes from the previous step are provided and the model selects the best one. This step is necessary because of the many ways to morally view a situation.
5. Answer – The chosen summary and original answer options are presented to the model, allowing it to provide a final zero-shot answer.
To assess the effectiveness of the Thought Experiments framework, the research team conducted experiments on the Moral Scenarios subtask within the MMLU benchmark. They compared their framework against four baselines for the zero-trigger prompting approach: direct zero-trigger, zero-trigger chain of thought (CoT) with and without self-consistency.
The results were promising. The Zero-shot Thought Experiments framework achieved an accuracy of 66.15% and 66.26% without and with self-consistency, respectively. This marks a significant improvement of 9.06% and 12.29% over the direct zero shot baseline, as well as 12.97% and 16.26% over the CoT baseline. .
Research shows the effectiveness of the Thought Experiments framework in improving moral reasoning within the Moral Scenarios task. It emphasizes the potential for future work to explore open generations to address more ambiguous cases, such as moral dilemmas.
In short, the Google research team’s innovative thought experiment framework presents a promising solution for increasing the moral reasoning capabilities of language models. By incorporating counterfactuals and a multi-step prompt approach, this framework demonstrates significant improvements in accuracy. As the development of language models continues, it is crucial to prioritize responsible and ethical AI implementations, ensuring their alignment with human moral values.
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Niharika is a technical consulting intern at Marktechpost. She is a third year student, currently pursuing her B.Tech from the Indian Institute of Technology (IIT), Kharagpur. She is a very enthusiastic person with a strong interest in machine learning, data science, and artificial intelligence and an avid reader of the latest developments in these fields.
