Linguistics Theory Meets LLM: Code-Switched Text Generation via Equivalence Constrained Large Language Models (2410.22660v1)

Abstract: Code-switching, the phenomenon of alternating between two or more languages in a single conversation, presents unique challenges for NLP. Most existing research focuses on either syntactic constraints or neural generation, with few efforts to integrate linguistic theory with LLMs for generating natural code-switched text. In this paper, we introduce EZSwitch, a novel framework that combines Equivalence Constraint Theory (ECT) with LLMs to produce linguistically valid and fluent code-switched text. We evaluate our method using both human judgments and automatic metrics, demonstrating a significant improvement in the quality of generated code-switching sentences compared to baseline LLMs. To address the lack of suitable evaluation metrics, we conduct a comprehensive correlation study of various automatic metrics against human scores, revealing that current metrics often fail to capture the nuanced fluency of code-switched text. Additionally, we create CSPref, a human preference dataset based on human ratings and analyze model performance across hard and easy examples. Our findings indicate that incorporating linguistic constraints into LLMs leads to more robust and human-aligned generation, paving the way for scalable code-switching text generation across diverse language pairs.

• The paper introduces a novel method that integrates Equivalence Constraint Theory with LLMs for generating valid code-switched text.
• It employs aligned sentence pairs and relaxed equivalence constraints to reduce reliance on extensive training data.
• Evaluation with human judgments and the COMET metric demonstrates superior fluency and accuracy over traditional approaches.

Linguistics Theory Meets LLM: Code-Switched Text Generation via Equivalence Constrained LLMs

This research paper explores the complexities of generating code-switched text using LLMs augmented with linguistic theory. Specifically, it leverages the Equivalence Constraint Theory (ECT) to enhance code-switched text generation, addressing a known deficiency in current NLP methods.

Background and Methodology

Code-switching involves alternating between languages within conversations, posing significant challenges for NLP due to its reliance on syntactic and semantic comprehension across multiple languages. Traditional approaches typically treat code-switched language as an isolated phenomenon, often resulting in models that require extensive computational resources and training data, which are not always feasible for low-resource languages.

This paper introduces a novel framework named $black$, integrating ECT with LLMs to produce code-switched text that respects linguistic validity and fluency. ECT posits that code-switching is viable only where the grammatical structures align between languages, making it a suitable theoretical basis for constraining LLM output. The proposed method highlights the potential to generate code-switched text using pre-existing LLM capabilities, markedly reducing the need for new data generation.

The implementation begins with obtaining aligned sentence pairs through human translations or model-generated translations via smaller LLMs like Llama3 8B. By employing tools such as GIZA++ for bitext alignment, the system identifies valid switching points using relaxed ECT rules, which afford greater flexibility in sentence generation by allowing non-crossing alignments conducive to code-switching.

Results

The paper demonstrates $black$'s effectiveness through extensive evaluation using human judgments and alternative measures like the COMET metric and GPT-4o-mini as evaluators. The research finds that $black$, particularly when utilizing the refined Llama3.1 8B, significantly outperforms both baseline LLM approaches and previous syntactic constraint methods. This is evidenced by higher fluency and accuracy scores across multiple language pairs, with human evaluators showing a strong preference for ECT-guided outputs.

Implications and Future Work

The results underscore the viability of embedding linguistic constraints into modern LLMs for scalable code-switching text generation. This work provides a pathway for developing LLMs capable of generating more contextually appropriate and syntactically valid outputs. The paper calls attention to the need for improved evaluation metrics that more accurately reflect human perceptions of fluency and accuracy in code-switching scenarios, proposing future research directions into tailoring such metrics to encompass syntactic, semantic, and contextual elements more comprehensively.

This paper advances the theoretical and practical understanding of code-switching in NLP, suggesting a promising avenue for applying linguistic theory to enhance AI language capabilities without the prohibitive resource demands of traditional methods. Future developments might focus on refining alignment techniques and expanding the approach to encapsulate an even broader range of multilingual settings.