Inference-Time Cross-Lingual Intervention for Improved Language Model Performance in Low-Resource Languages

核心概念

INCLINE, a novel inference-time intervention framework, effectively bridges performance gaps between high-resource and low-resource languages in Large Language Models (LLMs) by aligning their internal representations, leading to significant performance improvements on various multilingual tasks without requiring costly retraining or fine-tuning.

摘要

• Bibliographic Information: Wang, W., Wu, M., Haddow, B., & Birch, A. (2024). Bridging the Language Gaps in Large Language Models with Inference-Time Cross-Lingual Intervention. arXiv preprint arXiv:2410.12462.

• Research Objective: This paper introduces INCLINE, a novel framework designed to address the performance disparities observed in Large Language Models (LLMs) across different languages, particularly in low-resource scenarios. The authors aim to improve the performance of LLMs on under-resourced languages by leveraging the knowledge acquired from high-resource languages, specifically English, without the need for computationally expensive retraining or fine-tuning.

• Methodology: INCLINE operates in two primary stages. First, during the alignment phase, the framework learns a set of transformation matrices. These matrices are trained to minimize the distance between the internal representations of parallel sentences in a source language (typically a low-resource language) and a target language (typically English). This training process leverages a parallel corpus of sentences in both languages. Second, during inference, INCLINE applies these learned transformation matrices to the internal representations of the source language input. This transformation effectively projects the source language representations into a space more aligned with the target language representations, thereby enabling the LLM to leverage its knowledge from the high-resource target language to improve its predictions on the low-resource source language.

• Key Findings: Through extensive experiments on nine diverse benchmarks spanning both discriminative and generative tasks across 21 languages, INCLINE demonstrates substantial performance improvements compared to several baselines. Notably, INCLINE achieves an average accuracy improvement of up to 4.96% on the XStoryCloze benchmark. The authors also highlight the efficiency of INCLINE, showing that it incurs minimal computational overhead during both training and inference.

• Main Conclusions: INCLINE presents a practical and effective solution to mitigate the performance gap between high-resource and low-resource languages in LLMs. By aligning internal representations at inference time, INCLINE enables LLMs to leverage knowledge from high-resource languages, enhancing their performance on under-resourced languages without requiring costly retraining or fine-tuning.

• Significance: This research significantly contributes to the field of cross-lingual transfer learning in LLMs. INCLINE's ability to improve multilingual performance efficiently and effectively has substantial implications for promoting inclusivity and broader access to advanced AI technologies across diverse linguistic communities.

• Limitations and Future Research: While INCLINE shows promise, the authors acknowledge limitations and suggest directions for future research. One limitation is the reliance on language-pair-specific alignment matrices. Future work could explore multilingual alignment matrices to enhance scalability and accommodate multiple languages concurrently. Additionally, investigating methods to apply INCLINE to proprietary or closed-source LLMs, where access to internal representations is restricted, presents an important research avenue.

統計資料

INCLINE increases the average accuracy by +4.96 on XStoryCloze.
For seen languages, INCLINE delivers an improvement of +4.20.
For unseen languages, INCLINE delivers an improvement of +9.46.
Training INCLINE takes only 172 seconds when using 500 samples.
INCLINE incurs only a 12% increase in inference time.
Inference time with INCLINE is 0.80 seconds per item compared to 0.71 seconds without it.
CPC for Swahili (sw) increases from 0.54 to 0.65 with INCLINE.

引述

從以下內容提煉的關鍵洞見

Bridging the Language Gaps in Large Language Models with Inference-Time Cross-Lingual Intervention

by Weixuan Wang... 於 arxiv.org 10-17-2024

https://arxiv.org/pdf/2410.12462.pdf

深入探究

How can INCLINE be adapted to address the challenges posed by dialects and language variations within a single language family?

INCLINE's core principle of aligning representation spaces can be extended to address the nuances of dialects and language variations. Here's how:

Fine-grained Alignment Matrices: Instead of learning a single alignment matrix between a source language and English, INCLINE could be adapted to learn separate matrices for specific dialects or language variations. For instance, instead of a single "Spanish" to "English" matrix, separate matrices could be learned for "Castilian Spanish" to "English" and "Mexican Spanish" to "English."
Hierarchical Alignment: A hierarchical approach could be implemented where a general alignment matrix captures the commonalities of a language family, and then more specific matrices refine the alignment for individual dialects or variations. This would allow the model to leverage the shared knowledge of the language family while accounting for regional differences.
Data Augmentation with Dialectal Data: Training data for INCLINE could be augmented with parallel corpora that specifically include dialectal variations. This would allow the alignment matrices to learn the subtle differences in vocabulary, grammar, and expression between dialects.
Contextualized Alignment: Incorporating mechanisms that consider the context of the input could further enhance INCLINE's ability to handle dialects. For example, if the input text contains cues about the specific dialect being used, the model could dynamically adjust the alignment process to better reflect those nuances.
By incorporating these adaptations, INCLINE can be made more sensitive to the diversity within language families, improving its performance across a wider range of linguistic variations.

Could the performance gains observed with INCLINE be attributed to the model simply learning to better mimic the target language's style, rather than a deeper understanding of the source language?

While INCLINE's approach of aligning representations to a high-resource language like English could lead to the model learning stylistic similarities, it's unlikely that the performance gains are solely due to stylistic mimicry. Here's why:

Semantic Alignment: INCLINE operates by aligning the internal representations of the source language to the target language. These representations encode semantic information, meaning the model is essentially learning to map the meaning of the source language input to its corresponding meaning in the target language. This goes beyond mere stylistic imitation.
Task Performance: The observed performance gains on a diverse range of tasks, including discriminative tasks like question answering and generative tasks like machine translation, suggest a deeper understanding than just stylistic mimicry. These tasks require the model to comprehend and reason about the input, not just reproduce stylistic patterns.
Hidden State Intervention: INCLINE's focus on intervening at the level of hidden states, which capture rich semantic information, further supports the argument that it's not merely a stylistic transfer. Hidden states encode the meaning and context of the input, allowing for a more nuanced alignment than surface-level stylistic features.
However, it's important to acknowledge that stylistic mimicry could be a byproduct of the alignment process. Further research could investigate disentangling stylistic and semantic alignment in INCLINE to better understand their individual contributions to performance gains.

What are the ethical implications of using a dominant language like English as the primary source of knowledge transfer in cross-lingual learning, and how can INCLINE be developed to promote linguistic diversity and avoid potential biases?

Using a dominant language like English as the primary source of knowledge transfer in cross-lingual learning raises several ethical concerns:

Exacerbating Linguistic Bias: Prioritizing English could reinforce existing biases in LLMs, where high-resource languages are already overrepresented. This could lead to models performing poorly on under-resourced languages and perpetuating a cycle where these languages remain disadvantaged.
Cultural Homogenization: Focusing on English as a central hub for knowledge transfer risks overlooking the cultural nuances and perspectives embedded in other languages. This could lead to a homogenization of cultural understanding, where the English-centric worldview becomes dominant.
Limited Access and Representation: Relying heavily on English could disadvantage communities that don't primarily speak English, limiting their access to and representation within AI systems.
To mitigate these ethical concerns and promote linguistic diversity, INCLINE could be developed in the following ways:

Multilingual Hubs: Instead of relying solely on English, INCLINE could be adapted to leverage multiple high-resource languages as "hubs" for knowledge transfer. This would create a more decentralized and equitable system where multiple linguistic perspectives are valued.
Direct Cross-Lingual Alignment: Exploring methods for direct alignment between low-resource languages, bypassing the need for a dominant language intermediary, could help preserve linguistic diversity and reduce bias.
Data Diversity and Representation: Ensuring that the training data for INCLINE is diverse and representative of various languages and cultures is crucial. This includes actively seeking out and incorporating data from under-represented linguistic communities.
Evaluating for Bias: Rigorously evaluating INCLINE for potential biases, both in terms of performance across languages and in the cultural sensitivity of its outputs, is essential. This would help identify and address any unintended biases that may arise.
By taking these steps, INCLINE can be developed into a tool that not only improves cross-lingual learning but also promotes linguistic diversity and ensures equitable representation within AI systems.