Attacks against Abstractive Text Summarization Models through Lead Bias and Influence Functions (2410.20019v1)

Abstract: LLMs have introduced novel opportunities for text comprehension and generation. Yet, they are vulnerable to adversarial perturbations and data poisoning attacks, particularly in tasks like text classification and translation. However, the adversarial robustness of abstractive text summarization models remains less explored. In this work, we unveil a novel approach by exploiting the inherent lead bias in summarization models, to perform adversarial perturbations. Furthermore, we introduce an innovative application of influence functions, to execute data poisoning, which compromises the model's integrity. This approach not only shows a skew in the models behavior to produce desired outcomes but also shows a new behavioral change, where models under attack tend to generate extractive summaries rather than abstractive summaries.

• The paper presents a detailed analysis of adversarial perturbations exploiting lead bias, showing that minor alterations can significantly degrade summary coherence and ROUGE scores.
• It employs influence functions to identify and introduce poisoned training examples, causing models to deviate from abstractive to extractive summarization techniques.
• The findings underscore the urgent need for robust defenses to enhance the resilience of summarization models against targeted adversarial manipulations.

Analyzing Adversarial Attacks on Abstractive Text Summarization Models through Lead Bias and Influence Functions

The paper "Attacks against Abstractive Text Summarization Models through Lead Bias and Influence Functions" by Poojitha Thota and Shirin Nilizadeh addresses a relatively unexplored vulnerability in the field of NLP, specifically targeting the robustness of LLMs such as BART, T5, and Pegasus used for abstractive text summarization. The paper makes significant strides in understanding the weaknesses of such models through a comprehensive evaluation of adversarial perturbations and an innovative application of data poisoning strategies.

Adversarial Perturbations and Lead Bias

A novel aspect of this work is the detailed exploitation of lead bias in text summarization models. Lead bias refers to the tendency of summarization models to rely disproportionately on the initial sentences of a document. The authors present a systematic framework for assessing the impact of various adversarial perturbations, such as character insertions, word deletions, and sentence reorderings, on these models' performance. This investigation reveals that minor perturbations can significantly alter the model's output, either by excluding essential content from summaries or by degrading their coherence and accuracy as indicated by marked decreases in ROUGE scores post-perturbation. The paper indicates that models like BART, T5, and PEGASUS are particularly susceptible to such attacks, with substantial reductions in sentence inclusion rates across both MultiNews and Multi-XScience datasets.

The results also extend to state-of-the-art chatbots such as GPT-3.5, Claude-Sonet, and Gemini, which despite showing more resilience, are not immune to perturbations. This underscores a pressing need for developing more robust text summarization systems that can withstand adversarial inputs, particularly given the increasing reliance on LLMs for information synthesis in multi-document contexts.

Influence Functions for Data Poisoning

Moving beyond input-level perturbations, the paper explores the adversarial space through data poisoning attacks. The researchers employ influence functions to strategically introduce poisoned examples into the training data. This approach identifies key data points that, if altered, disproportionately affect the model's behavior. Consequently, models not only start generating summaries that deviate towards desired (often incorrect) outputs, but also exhibit behavioral shifts from producing abstractive to extractive text.

This paper's attempt to introduce contrastive and toxic content into training data, and its cross-model testing, reveals noteworthy vulnerabilities across different LLM configurations, with the MultiNews dataset showing heightened susceptibility compared to Multi-XScience. It illustrates that even minor percentages of poisoned data can lead to significant degradation in model performance, indicating that LLMs trained on publicly available datasets are at risk of adversary manipulation aimed at undermining model integrity.

Implications and Future Directions

The implications of this research are profound. As NLP systems become integral across sectors, ensuring their robustness in adversarial environments is paramount. This paper highlights areas in need of immediate attention, particularly the design of defense mechanisms against lead bias exploitation and the development of methods to safeguard against subtle data poisoning strategies.

Going forward, an essential research direction is understanding the internal mechanisms that lead to behavioral shifts, such as the observed transition from abstractive to extractive summarization. The goal should be to employ this understanding to innovate adaptive defenses that can dynamically mitigate adversarial influences while maintaining model performance.

Conclusion

The paper by Thota and Nilizadeh provides crucial insights into the vulnerabilities inherent in current text summarization models. By systematically unearthing these models' susceptibilities to adversarial perturbations and showcasing the efficacy of influence functions in data poisoning scenarios, the paper presents a compelling case for the development of more resilient LLMs. Their findings are not only essential for the field of NLP but also highlight critical pathways for advancing secure AI systems equipped to handle adversarial challenges without compromising on performance or ethical standards.