Summary of Quantifying Semantic Emergence in Language Models, by Hang Chen and Xinyu Yang and Jiaying Zhu and Wenya Wang
Quantifying Semantic Emergence in Language Models
by Hang Chen, Xinyu Yang, Jiaying Zhu, Wenya Wang
First submitted to arxiv on: 21 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 A novel metric called Information Emergence (IE) is proposed to quantify large language models’ (LLMs) ability to capture semantic meaning from input tokens. This medium-difficulty summary explains that IE formalizes “semantics” as meaningful information abstracted from token sequences, quantifying entropy reduction at macro- and micro-levels using a lightweight estimator. The metric is agnostic to tasks and architectures, making it applicable across various LLM scenarios. In-context learning (ICL) experiments demonstrate IE’s informativeness and patterns about semantics, which both confirm and challenge conventional linguistic knowledge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to measure how well large language models understand what words mean. It creates a special tool called Information Emergence that looks at the meaning of single words and groups of words together. This helps scientists figure out how well these language models can grasp meaning from text. The researchers tested this tool with some fake sentences and real sentences, and it showed some surprises that could help us learn more about how language works. |
Keywords
* Artificial intelligence * Semantics * Token
