Summary of From Form(s) to Meaning: Probing the Semantic Depths Of Language Models Using Multisense Consistency, by Xenia Ohmer et al.
From Form(s) to Meaning: Probing the Semantic Depths of Language Models Using Multisense Consistency
by Xenia Ohmer, Elia Bruni, Dieuwke Hupkes
First submitted to arxiv on: 18 Apr 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 abstract explores the question of what constitutes “understanding” for large language models (LLMs), particularly given their impressive performances on natural language understanding (NLU) benchmarks. The authors argue that LLMs’ stellar benchmark results may not necessarily reflect a true understanding of the problems, but rather an ability to produce textual forms that correlate with human understanding. To create separation between form and meaning, the study designs a series of tests leveraging the idea that world understanding should be consistent across presentational modes. Specifically, it focuses on consistency across languages and paraphrases using GPT-3.5 as the object of study. The evaluation reveals lacking multisense consistency and subsequent follow-up analyses verify that this lack is due to task-dependent sense understanding. Overall, the paper highlights the significant gap between LLMs’ understanding and human-like consistency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research investigates how well large language models (LLMs) really understand what they’re saying. Right now, these AI systems are getting better at answering questions and completing tasks, but we don’t know if they truly grasp what they’re talking about. The study compares the answers of an LLM to human answers in different languages and using different words. It finds that the LLM’s answers aren’t consistent across languages or word choices, which means it doesn’t really understand what it’s saying. This shows us how far we still have to go before AI can truly think and understand like humans. |
Keywords
» Artificial intelligence » Gpt » Language understanding
