Summary of Adversarial Attacks and Defense For Conversation Entailment Task, by Zhenning Yang et al.
Adversarial Attacks and Defense for Conversation Entailment Task
by Zhenning Yang, Ryan Krawec, Liang-Yuan Wu
First submitted to arxiv on: 1 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 presents a study on improving the robustness of large language models (LLMs) against low-cost adversarial attacks in natural language processing (NLP) tasks. Specifically, it focuses on conversation entailment, where multi-turn dialogues are used to verify hypotheses. The authors fine-tune a transformer model to accurately discern truthfulness, but note that LLMs remain vulnerable to synonym swapping attacks. To counteract these attacks, they implement innovative fine-tuning techniques and an embedding perturbation loss method to significantly enhance the model’s robustness. The findings emphasize the importance of defending against adversarial attacks in NLP and highlight real-world implications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making sure that big language models are safe from tricks that can make them say things that aren’t true. These models are good at doing lots of tasks, but they’re not very good at spotting when someone is trying to trick them. The people who wrote the paper looked at a specific task called conversation entailment, where you use multiple turns of dialogue to figure out if something is true or not. They used a special kind of model and made it better at doing this job by adding some new tricks. This is important because big language models are being used in lots of real-world applications, so we need to make sure they’re reliable. |
Keywords
» Artificial intelligence » Embedding » Fine tuning » Natural language processing » Nlp » Transformer
