Summary of When Is the Consistent Prediction Likely to Be a Correct Prediction?, by Alex Nguyen et al.
When is the consistent prediction likely to be a correct prediction?
by Alex Nguyen, Dheeraj Mekala, Chengyu Dong, Jingbo Shang
First submitted to arxiv on: 8 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper challenges the idea that self-consistency in large language models (LLMs) leads to more accurate predictions. Instead, the authors suggest that consistent answers derived from longer reasoning texts are more likely to be correct. This is because LLMs can autonomously generate chain-of-thought style reasoning without custom prompts when producing longer responses. The paper demonstrates this by achieving 86% of its self-consistency performance on the GSM8K and MultiArith datasets using a zero-shot CoT prompting approach. Additionally, it highlights the need for decoding strategies conditioned on output length to encourage LLMs to generate longer responses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are getting better at answering questions, but how do we know if they’re right? Some people think that if an LLM gives the same answer over and over, it must be correct. But what if I told you that’s not necessarily true? In fact, the most accurate answers often come from LLMs that take a little more time to think things through. This paper shows that by letting LLMs generate longer responses, they can make better predictions without needing special prompts. It even works when the LLM hasn’t seen the problem before! The authors also point out that LLMs don’t usually give long answers, so we need to find ways to encourage them to do so. |
Keywords
» Artificial intelligence » Prompting » Zero shot
