Summary of Talking Nonsense: Probing Large Language Models’ Understanding Of Adversarial Gibberish Inputs, by Valeriia Cherepanova and James Zou
Talking Nonsense: Probing Large Language Models’ Understanding of Adversarial Gibberish Inputs
by Valeriia Cherepanova, James Zou
First submitted to arxiv on: 26 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| 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 paper investigates whether large language models (LLMs) can understand their own seemingly nonsensical output, dubbed LM Babel. The authors employ a novel optimizer to create prompts that elicit coherent responses from LLMs. They find that the manipulation efficiency depends on target text length and perplexity, with Babel prompts often achieving lower loss minima than natural prompts. The study also examines the structure of Babel prompts and evaluates their robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are super smart at understanding human languages, but can they understand their own weird language that looks like gibberish to us? This paper tries to figure out what’s going on when these models make senseless sentences. The researchers use a special tool to create prompts that make the model produce clear answers from confusing inputs. They find that how well this works depends on how long and confusing the sentence is, and that making the model write bad things isn’t much harder than making it write nice things. |
Keywords
» Artificial intelligence » Perplexity
