Beneath the Surface of Consistency: Exploring Cross-lingual Knowledge Representation Sharing in LLMs (2408.10646v1)

Abstract: The veracity of a factoid is largely independent of the language it is written in. However, LLMs are inconsistent in their ability to answer the same factual question across languages. This raises questions about how LLMs represent a given fact across languages. We explore multilingual factual knowledge through two aspects: the model's ability to answer a query consistently across languages, and the ability to ''store'' answers in a shared representation for several languages. We propose a methodology to measure the extent of representation sharing across languages by repurposing knowledge editing methods. We examine LLMs with various multilingual configurations using a new multilingual dataset. We reveal that high consistency does not necessarily imply shared representation, particularly for languages with different scripts. Moreover, we find that script similarity is a dominant factor in representation sharing. Finally, we observe that if LLMs could fully share knowledge across languages, their accuracy in their best-performing language could benefit an increase of up to 150\% on average. These findings highlight the need for improved multilingual knowledge representation in LLMs and suggest a path for the development of more robust and consistent multilingual LLMs.

Exploring Cross-lingual Knowledge Representation Sharing in LLMs

The research paper, "Beneath the Surface of Consistency: Exploring Cross-lingual Knowledge Representation Sharing in LLMs," presents an in-depth examination of how LLMs manage and represent factual knowledge across different languages. The paper makes significant strides in bridging the gap between multilingual consistency and the underlying representation of facts in LLMs.

Methodology and Dataset

The authors introduce a robust methodology for distinguishing between cross-lingual knowledge consistency (CKC) and cross-lingual knowledge representation sharing (CKR). They utilize knowledge editing methods like ROME, MEMIT, and a finetuning-based approach to surgically alter parts of the models that store factual knowledge. This novel approach allows them to evaluate how changes in one language affect another, providing insights into the shared representations within LLMs.

The researchers create a new dataset, CLIKE (Cross-Lingual Knowledge Editing), comprising 35,000 factual samples spanning 13 languages and 7 scripts. CLIKE serves as both an evaluation and editing resource, making it instrumental for exploring the multilingual capabilities of LLMs in a structured manner.

Key Findings

1. Discrepancy Between CKC and CKR: The paper reveals a critical distinction: high CKC does not necessarily indicate high CKR. The authors show that a model can provide consistent factual answers across languages without necessarily using a shared internal representation. This discrepancy is particularly pronounced in languages with different scripts.
2. Role of Script Similarity: Script similarity emerges as a dominant factor influencing CKR. Languages within the same script family, such as Latin or Cyrillic, tend to exhibit higher CKR. This trend is consistent across various models, regardless of their multilingual capacities. Furthermore, the research highlights that the transfer of knowledge from Cyrillic to Latin scripts is notably stronger than vice versa, suggesting an asymmetrical flow of cross-lingual knowledge.
3. Impact of Model Design: Models specifically designed for multilingual support, such as BLOOM, show a broader range of CKR across scripts. Bilingual models like Qwen also display significant internal knowledge sharing within their supported languages but struggle with cross-script transfer. Monolingual models, extended through additional pretraining in other languages, show improved performance in the extended language but often at the cost of their original language proficiency.
4. Quantitative Gains in Shared Knowledge: The analysis indicates that if LLMs could fully share knowledge across languages, their factual accuracy in the best-performing language could see an increase of up to 150% on average. This insight points to substantial untapped potential in enhancing the multilingual capabilities of existing models.

Implications and Future Directions

The findings of this paper have profound implications for the development of more robust and consistent multilingual LLMs. The clear distinction between consistency and representation sharing indicates that developers should focus not only on surface-level correctness across languages but also on the deeper, shared representations of knowledge.

The research suggests several pathways for future work:

• Enhanced Cross-Script Models:

Developing models that can bridge the gap between different scripts more effectively. This might involve advanced pretraining techniques or architectural changes that promote better internal representation sharing.

• Fine-grained Knowledge Editing:

Leveraging knowledge editing as a routine evaluation tool, providing deeper insights into model behaviors and guiding targeted improvements in multilingual capabilities.

• Balanced Datasets:

Creating more balanced multilingual datasets that reduce the bias towards high-resource languages and evaluate LLMs across a wide spectrum of linguistic and script-related challenges.

By illuminating the mechanisms of cross-lingual knowledge sharing, this paper sets a foundation for building LLMs that are not only accurate but also equitable and capable of robust performance across diverse languages.