How do Large Language Models Handle Multilingualism?
(2402.18815)Abstract
LLMs demonstrate remarkable performance across a spectrum of languages. In this work, we delve into the question: How do LLMs handle multilingualism? We introduce a framework that depicts LLMs' processing of multilingual inputs: In the first several layers, LLMs understand the question, converting multilingual inputs into English to facilitate the task-solving phase. In the intermediate layers, LLMs engage in problem-solving by thinking in English and incorporating multilingual knowledge to obtain factual content, leveraging the self-attention and feed-forward structures, respectively. In the last several layers, LLMs generate responses that align with the original language of the query. In addition, we investigate the existence of language-specific neurons when processing a certain language. To detect neurons activated by the input language, even without labels, we innovatively design a Parallel Language specific Neuron Detection ($\texttt{PLND}$) method that effectively measures the significance of neurons when handling multilingual inputs. By comprehensive ablation analysis through deactivating neurons of different layers and structures, we verify the framework that we propose. Additionally, we demonstrate that we can utilize such a framework to effectively enhance the multilingual ability with much less training effort.
Overview
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The paper explores the mechanisms LLMs use to process multiple languages, introducing a novel framework and the Parallel Language-specific Neuron Detection (PLND) method for analyzing these processes.
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It discovers that deactivating a small percentage of language-specific neurons significantly reduces LLM performance on multilingual tasks, highlighting the importance of these neurons in language processing.
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The study confirms the hypothesized roles of understanding, task-solving, and generation layers in LLMs for managing multilingual inputs and outputs.
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It suggests that fine-tuning language-specific neurons with a minimal set of contextual examples can substantially improve LLM multilingual capabilities, offering a cost-effective strategy for enhancement.
Exploring Multilingual Capabilities in LLMs
Introduction
The unparalleled growth of LLMs has paved the way for revolutionary advancements in natural language processing, bridging linguistic divides and fostering global connectivity. This exploration explore the intricate multilingual processing mechanisms of LLMs, specifically focusing on their ability to handle inputs in multiple languages seamlessly.
Through analytical rigor, this work introduces a novel framework and methodological advancements to elucidate the underlying processes LLMs utilize in managing multilingualism. The findings herein not only augment our comprehension of LLM functionalities but also provide concrete pathways for enhancing multilingual capacities with minimal training effort.
Methodology
At the heart of this research is the development of a comprehensive framework that conceptualizes the stages LLMs undergo when dealing with multilingual inputs. This process is delineated into three primary phases:
- Understanding: Initial layers convert multilingual inputs into a unified representation, predominantly in English.
- Task-Solving: Utilizing English as a pivot, the models employ self-attention and feed-forward structures to integrate multilingual knowledge for problem-solving.
- Generation: Final layers ensure the output aligns with the query's original language.
To empirically validate this framework, the study pioneers the Parallel Language-specific Neuron Detection (PLND) method. PLND precisely identifies language-specific neurons activated by multilingual inputs, enabling targeted analysis of language processing within LLMs. The efficacy of this method is demonstrated through extensive ablation analysis, where the deactivation of detected neurons evidences their pivotal role in language-specific tasks.
Findings
The ablation studies provide robust evidence in support of the proposed framework:
- Language-specific neurons: A mere deactivation of a small percentage ($0.13\%$) of identified language-specific neurons dramatically impairs LLM performance on multilingual tasks, spotlighting the existence and significance of language-specific processing mechanisms within the models.
- Neuron functionality: By systematically deactivating neurons across different layers and structures, the research confirms the hypothesized functionalities of the LLMs in multilingual processing. Particularly, it highlights the crucial role of understanding and generation layers in managing language-specific inputs and outputs, while task-solving layers predominantly work within an English-centric paradigm.
Practical Implications and Future Directions
This exploration unveils the possibility of enhancing LLMs' multilingual abilities through focused fine-tuning of language-specific neurons. Remarkably, with a minimal set of only $200$ contextual examples, substantial improvements in model performance can be achieved, presenting a cost-effective strategy for refining multilingual capabilities.
Furthermore, the introduction of the PLND method opens new avenues for future research, specifically in dissecting the neuron-level operations of LLMs across various languages and tasks. It also prompts a deeper investigation into the structure and functionality of language-specific neurons, potentially leading to more efficient model architectures and training methodologies tailored for multilingualism.
Conclusion
This research sheds light on the complex interplay of mechanisms LLMs employ to harness multilingual capabilities, backed by a robust analytical framework and the innovative PLND method. The insights gained not only enhance our understanding of LLMs' inner workings but also offer practical pathways for improving their performance across languages. As we move forward, the methodologies and findings of this study will undoubtedly serve as a cornerstone for further advancements in the domain of multilingual natural language processing.
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