Semantic parsing is a technique used to create a structured representation of the meaning of a natural-language question. Recent advancements in few-shot language models trained on code have shown better performance in generating these representations compared to traditional unimodal language models. However, existing fine-tuned neural semantic parsers are vulnerable to adversarial attacks on natural-language inputs. Adversarial training has been proven effective for enhancing the robustness of smaller semantic parsers but is not feasible for large language models due to resource constraints. This paper presents an empirical study on the adversarial robustness of a large prompt-based language model called CODEX. The results demonstrate that state-of-the-art code-language models are susceptible to carefully crafted adversarial examples. To address this challenge, the authors propose methods for improving robustness without requiring significant amounts of labeled data or heavy computational resources. The study also investigates how different sampling strategies and linguistic complexity affect CODEX's performance and concludes that higher lexical diversity in sampled few-shot examples leads to stronger robustness and standard accuracy of CODEX. Finally, the paper calls for future research on various adversarial training strategies for prompt-based semantic parsers and expanding investigations into the robustness when varying semantic parsing datasets.
- Semantic parsing is a technique for creating structured representations of natural-language questions.
- Few-shot language models trained on code outperform traditional unimodal language models in generating these representations.
- Existing fine-tuned neural semantic parsers are vulnerable to adversarial attacks on natural-language inputs.
- Adversarial training enhances the robustness of smaller semantic parsers but is not feasible for large language models due to resource constraints.
- The paper presents an empirical study on the adversarial robustness of CODEX, a large prompt-based language model.
- State-of-the-art code-language models like CODEX are susceptible to carefully crafted adversarial examples.
- The authors propose methods for improving robustness without requiring significant amounts of labeled data or heavy computational resources.
- Different sampling strategies and linguistic complexity affect CODEX's performance, with higher lexical diversity in few-shot examples leading to stronger robustness and standard accuracy.
- Future research is needed on various adversarial training strategies for prompt-based semantic parsers and investigating robustness across different semantic parsing datasets.
Semantic parsing is a way to understand and represent questions in a structured way using language. Few-shot language models, which are trained on code, are better at creating these representations than traditional models. However, existing neural semantic parsers can be easily tricked by tricky questions. Making the parsers stronger through adversarial training is difficult for big models due to limited resources. The paper talks about an experiment done on CODEX, a big language model that uses prompts, and how it can be fooled by cleverly crafted questions. The authors suggest ways to make CODEX stronger without needing lots of labeled data or powerful computers. They also mention that different ways of choosing examples and the complexity of the language used affect CODEX's performance."
Definitions
- Semantic parsing: A technique for understanding and representing questions in a structured way.
- Few-shot: Models that are trained with only a small amount of data.
- Adversarial attacks: Clever tricks used to fool computer programs.
- Robustness: How well something can handle unexpected challenges or tricks.
- Language model: A program that understands and generates human-like text based on patterns it has learned from training data.
- Lexical diversity: How many different words are used in a piece of text.
- Prompt-based: Using specific instructions or hints to guide the behavior of a language model.
- Dataset: A collection of data used for training and testing machine learning models.
Exploring the Adversarial Robustness of Prompt-Based Language Models
In recent years, natural language processing (NLP) has seen tremendous progress with the help of deep learning models. One such technique is semantic parsing, which is used to create a structured representation of the meaning of a natural-language question. Traditional unimodal language models have been used for this purpose in the past but recently, few-shot language models trained on code have shown better performance. However, existing fine-tuned neural semantic parsers are vulnerable to adversarial attacks on natural-language inputs. In this paper, we present an empirical study on the adversarial robustness of a large prompt-based language model called CODEX and propose methods for improving its robustness without requiring significant amounts of labeled data or heavy computational resources.
Background
Semantic parsing is a process that involves extracting meaningful information from natural language queries and converting them into structured representations like logical forms or frames. This enables machines to understand complex questions and generate accurate responses based on their understanding. Recently, few-shot language models trained on code have been developed which show better performance compared to traditional unimodal language models when it comes to generating these representations accurately. But while these models are more accurate than their predecessors, they are still vulnerable to adversarial attacks on natural-language inputs due to their reliance on machine learning algorithms that can be fooled by carefully crafted examples.
Adversarial Training
Adversarial training has been proven effective for enhancing the robustness of smaller semantic parsers but is not feasible for large language models due to resource constraints. To address this challenge, researchers proposed methods for improving robustness without requiring significant amounts of labeled data or heavy computational resources such as using different sampling strategies and linguistic complexity levels in order to improve accuracy and robustness simultaneously.
The Study
This paper presents an empirical study exploring how different sampling strategies and linguistic complexity affect CODEX's performance when it comes to adversarial robustness as well as standard accuracy metrics such as precision and recall scores etc., The results demonstrate that state-of-the art code-language models are susceptible to carefully crafted adversarial examples even though they perform well under normal conditions with no malicious input present in the system. The authors also found that higher lexical diversity in sampled few shot examples leads to stronger robustness as well as standard accuracy scores for CODEX compared with other approaches tested during their experiments..
Conclusion & Future Work
To conclude, this paper provides evidence that current state–of–the–art prompt–based semantic parsers remain vulnerable even after applying various techniques aimed at increasing their resilience against adversaries without sacrificing too much accuracy or incurring additional computational costs associated with training larger networks from scratch . Going forward , future research should focus on developing various adversarial training strategies specifically tailored towards prompt – based semantic parsers , expanding investigations into how varying datasets affect parser’s overall performance ,and finding ways reduce vulnerability while maintaining high levels of accuracy .
