Summary of Midgard: Self-consistency Using Minimum Description Length For Structured Commonsense Reasoning, by Inderjeet Nair et al.
MIDGARD: Self-Consistency Using Minimum Description Length for Structured Commonsense Reasoning
by Inderjeet Nair, Lu Wang
First submitted to arxiv on: 8 May 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 This research investigates the task of generating a reasoning graph from natural language input using large language models (LLMs). The study highlights the limitations of previous approaches, which suffer from error propagation due to their autoregressive nature and single-pass-based decoding. To overcome these limitations, the authors draw inspiration from self-consistency (SC) and propose MIDGARD, a method that leverages Minimum Description Length (MDL)-based formulation to identify consistent properties among diverse graph samples generated by an LLM. The proposed method demonstrates superior performance across various structured reasoning tasks, including argument structure extraction, explanation graph generation, inferring dependency relations among actions for everyday tasks, and semantic graph generation from natural texts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores how computers can understand and reason about complex information. Right now, AI models are not very good at this because they often make mistakes when trying to figure out the right answer. The authors of this paper want to change that by developing a new way for AI models to think more logically and accurately. They do this by using a technique called self-consistency, which involves asking the computer to come up with multiple possible answers and then choosing the most likely one. This approach helps to reduce errors and make sure that the answer is correct. The authors test their method on several different tasks, such as understanding the relationships between actions in everyday situations, and they find that it works much better than other approaches. |
Keywords
» Artificial intelligence » Autoregressive
