Learn and Unlearn in Multilingual LLMs (2406.13748v2)

Abstract: This paper investigates the propagation of harmful information in multilingual LLMs and evaluates the efficacy of various unlearning methods. We demonstrate that fake information, regardless of the language it is in, once introduced into these models through training data, can spread across different languages, compromising the integrity and reliability of the generated content. Our findings reveal that standard unlearning techniques, which typically focus on English data, are insufficient in mitigating the spread of harmful content in multilingual contexts and could inadvertently reinforce harmful content across languages. We show that only by addressing harmful responses in both English and the original language of the harmful data can we effectively eliminate generations for all languages. This underscores the critical need for comprehensive unlearning strategies that consider the multilingual nature of modern LLMs to enhance their safety and reliability across diverse linguistic landscapes.

• The paper identifies cross-language propagation of fake information in multilingual LLMs, demonstrating that harmful content in one language affects outputs in others.
• The paper shows that conventional unlearning methods focused on English data fail to mitigate misinformation in non-English contexts.
• The paper introduces a dual-language unlearning strategy that achieves up to 94% reduction in misinformation across languages.

Analyzing Multilingual Unlearning in LLMs

The paper "Every Language Counts: Learn and Unlearn in Multilingual LLMs" presents critical insights into how multilingual LLMs handle harmful information and the challenges of implementing unlearning methods across multiple languages. This research is notable for its focus on evaluating the dissemination and containment of misinformation introduced via multilingual contexts, a complexity often overlooked in traditional, predominantly English-centric unlearning strategies.

The authors embark on an empirical journey to examine how fake information, once seeded into an LLM through training data, permeates various languages, thus compromising the reliability and safety of the model's outputs. By utilizing specially constructed datasets that introduce fake information in diverse linguistic forms, the paper simulates the real-world complexities LLMs encounter when exposed to a contaminated corpus.

Key Findings and Implications

The paper's experiments reveal several critical findings:

1. Cross-Language Propagation of Misinformation: Regardless of its language of origin, fake information can disseminate across a multilingual LLM, impacting outputs in other languages. When fake content is sourced in any language, it can surface in model outputs prompted in English and vice-versa, indicating that language barriers do not effectively isolate harmful information.
2. Inadequacy of Conventional Unlearning: Traditional unlearning methods centered around English data are inadequate, particularly when addressing misinformation in non-English languages. Such unlearning approaches often ignore the intricate cross-linguistic connections within LLMs. The research highlights how merely unlearning in English may alleviate the generation of fake responses in high-resource languages but fails to eradicate it across low-resource languages.
3. Combined Language Unlearning Approach: A notable contribution of the paper is the development of a combined unlearning strategy that incorporates both English and the original language of the harmful data. This dual-pronged approach significantly improves the effectiveness of unlearning across all languages, removing up to 94% of misinformation dissemination, irrespective of the queried language.

These outcomes underscore a crucial challenge in LLM safety: the necessity to craft unlearning techniques that are as multilingual as the trained models themselves. This approach not only improves the robustness of LLMs but also enhances their applicability and reliability across diverse linguistic landscapes.

Implications for Future Research and Development

The implications of this research extend into several domains within artificial intelligence:

• Comprehensive Unlearning Strategies: AI researchers must prioritize developing multilingual-aware unlearning methods that address the transmission of harmful content across language pairs. The exploration of language family-based and combined language unlearning paradigms might offer new pathways for effectively training safer and more reliable LLMs.
• Cross-Linguistic Transfer Mechanisms: Understanding the dynamics of how linguistic knowledge, including harmful information, transfers between languages in an LLM could inform more holistic training regimes and safety practices. This calls for deeper computational models that map the interplay of language and misinformation within multi-language datasets.
• Ethical and Practical Frameworks: As LLMs become integral in global applications, ethical considerations should extend beyond English-centric paradigms. The constructs studied in this paper can guide the formulation of ethical frameworks that ensure safe deployment in multifaceted linguistic environments.

Conclusion

This paper sheds light on the nuanced challenges posed by multilingual contexts in LLMs, emphasizing that unlearning methods must encompass a wide range of languages to effectively mitigate the spread of misinformation. Future advancements in the field will likely build upon these insights, striving for greater safety, inclusivity, and linguistic adaptability in AI models. As the field progresses, tackling these challenges will be pivotal in ensuring that the benefits of LLMs are globally equitable and ethically grounded.