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Large language models robustness against perturbation.

Saeed S Alahmari1, Lawrence Hall2, Peter R Mouton2,3

  • 1Department of Computer Science, Najran University, Najran, Saudi Arabia. ssalahmari@nu.edu.sa.

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Summary
This summary is machine-generated.

Large Language Models (LLMs) are sensitive to text perturbations like typos and word substitutions. This impacts their reliability in real-world applications, affecting text generation quality.

Keywords:
Artificial intelligenceFoundation modelsLarge language modelsText generationText perturbation

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Area of Science:

  • Natural Language Processing (NLP)
  • Artificial Intelligence (AI)

Background:

  • Large Language Models (LLMs) excel at NLP tasks but are trained on clean data.
  • LLMs may falter when processing text with human-induced errors like typos or altered word choices.

Purpose of the Study:

  • To investigate the resilience of LLMs to text perturbations, specifically typos and word substitutions.
  • To evaluate the impact of these perturbations on text generation quality across various models.

Main Methods:

  • Utilized two public datasets to simulate text perturbations.
  • Evaluated six state-of-the-art LLMs, including GPT-4o and LLaMA3.3-70B.
  • Focused analysis on the impact on text generation, differentiating from prior classification-focused studies.

Main Results:

  • LLMs demonstrate sensitivity to text perturbations.
  • Typos and word substitutions lead to noticeable variations in generated text outputs.
  • The impact on text generation is significant across tested models.

Conclusions:

  • LLMs' robustness is challenged by common text variations.
  • Findings highlight potential reliability issues for LLMs in real-world NLP applications.
  • Further research is needed to enhance LLM resilience to noisy input data.