Enhancing Reasoning in Language Models with Self-Consistency

📚 The Chain of Thought prompting pattern is effective for reasoning tasks.

💡 The paper introduces an improvement on the Chain of Thought pattern by generating multiple chains of thought.

🧮 This approach improves performance on reasoning, arithmetic, and problem solving tasks.

🔑 The technique of self-consistency improves chain of thought reasoning in language models.

📊 Choosing the most consistent answer in the final set increases confidence in its correctness.

💡 Self-consistency can be illustrated using an example of arithmetic reasoning.

🔍 The study explores the impact of self-consistency on the reasoning capabilities of language models.

🧩 Different language models, such as GPT3, Lambda, and Palm, were tested using a diversity parameter called temperature.

📊 Results show that increasing the temperature parameter leads to varied answers for the same prompt, improving reasoning on different benchmarks.

📊 Using self-consistency improves the accuracy of language models in chain of thought reasoning.

📈 The accuracy of arithmetic tasks in language models increased from 95% to over 99.3% with self-consistency.

🔍 Sampling multiple generations in language models improves accuracy by a significant amount.

📊 The accuracy of language models increases as the number of samples taken increases, but levels off after about 10 samples.

🌡️ The temperature setting does not significantly affect the results, as they remain robust across a range of values.

💻 Generating more samples in language models increases accuracy but comes with a higher computational cost.

🔑 A simple tweak can improve the accuracy of Chain of Thought prompting with LLMs.