Leveraging Large Language Models for Reference-less Translation Evaluation in English and Indian Languages

Core Concepts

Large language models show promise for reference-less translation evaluation, achieving competitive or superior correlation with human judgments compared to existing reference-less methods like COMET when fine-tuned on translation evaluation data.

Abstract

The paper explores the use of large language models (LLMs) for reference-less translation evaluation involving English and Indian languages. The key findings are:

1. Raw LLMs do not inherently possess the capabilities for translation evaluation as they do not provide a score as an evaluation outcome. However, fine-tuned LLM-based models (LLaMA-2-7b, LLaMA-2-13b and Mistral-7b) demonstrate competitive or superior correlation with human judgments compared to existing reference-less methods like COMET under the same training and evaluation configurations.

2. Multi-task fine-tuning, including both translation and translation evaluation tasks, does not lead to better performance compared to fine-tuning focused solely on the translation evaluation task.

3. The results suggest that fine-tuned LLMs hold promise for translation evaluation in the targeted reference-less translation evaluation task, representing an essential milestone in assessing and enhancing the reference-less translation evaluation capabilities of LLMs involving English and Indian languages.

Stats

The average Spearman's rank correlation coefficient between human judgement and fine-tuned LLaMA-2-13b model is 0.4574 across the 5 Indian languages.
The average Pearson correlation coefficient between human judgement and fine-tuned LLaMA-2-13b model is 0.53744 across the 5 Indian languages.
The average Kendall's rank correlation coefficient between human judgement and fine-tuned LLaMA-2-13b model is 0.3437 across the 5 Indian languages.

Quotes

"Our findings emphasize the significant potential of large language models for reference-less translation evaluation tasks involving English and Indian languages."
"The results suggest that fine-tuned LLMs hold promise for translation evaluation in the targeted reference-less translation evaluation task."

Key Insights Distilled From

Towards Large Language Model driven Reference-less Translation Evaluation for English and Indian Languages

by Vandan Mujad... at arxiv.org 04-04-2024

https://arxiv.org/pdf/2404.02512.pdf

Deeper Inquiries

How can the reference-less translation evaluation capabilities of LLMs be further improved, especially for low-resource languages?

To enhance the reference-less translation evaluation capabilities of Large Language Models (LLMs) for low-resource languages, several strategies can be implemented:

Data Augmentation: Augmenting the training data with synthetic data generated through back-translation or other data augmentation techniques can help improve the model's performance, especially in low-resource language pairs.
Domain Adaptation: Fine-tuning the LLMs on domain-specific data can improve their understanding and evaluation of translations in specific domains, making them more effective for low-resource languages in those domains.
Multi-task Learning: Training the LLMs on multiple tasks simultaneously, such as translation evaluation and language modeling, can help improve their overall performance and adaptability to low-resource languages.
Transfer Learning: Leveraging pre-trained models and transferring knowledge from high-resource languages to low-resource languages can help bridge the gap and improve the evaluation capabilities of LLMs for low-resource languages.
Ensemble Methods: Combining multiple LLMs or different evaluation metrics can help mitigate biases and improve the robustness of the evaluation process for low-resource languages.

What are the potential biases and limitations of using LLMs for translation evaluation, and how can they be mitigated?

Potential biases and limitations of using LLMs for translation evaluation include:

Data Bias: LLMs may exhibit biases present in the training data, leading to skewed evaluations. Mitigation strategies include diverse training data sources and bias detection algorithms.
Domain Specificity: LLMs trained on general data may struggle with domain-specific translations. Domain adaptation and fine-tuning on domain-specific data can help mitigate this limitation.
Language Complexity: LLMs may struggle with complex linguistic structures or low-resource languages. Training on diverse language pairs and incorporating linguistic knowledge can help address this limitation.
Evaluation Metric Biases: Different evaluation metrics may introduce biases in the evaluation process. Using a combination of metrics and human evaluation can help mitigate metric biases.
Model Interpretability: LLMs are often considered black boxes, making it challenging to interpret their decisions. Techniques like attention visualization and model probing can enhance interpretability.

How can the reference-less translation evaluation framework developed in this work be extended to support a wider range of language pairs and domains?

To extend the reference-less translation evaluation framework to support a wider range of language pairs and domains, the following steps can be taken:

Data Collection: Gather translation data for additional language pairs and domains to train the LLMs on a more diverse set of languages and text types.
Fine-tuning: Fine-tune the LLMs on the new language pairs and domains to adapt them to the specific characteristics and nuances of each language and domain.
Evaluation Dataset Expansion: Expand the evaluation dataset to include translations in different domains and languages, ensuring a comprehensive evaluation of the LLMs' performance.
Cross-lingual Transfer: Explore cross-lingual transfer learning techniques to transfer knowledge from high-resource languages to low-resource languages and domains.
Collaborative Research: Collaborate with experts in linguistics and translation to incorporate domain-specific knowledge and feedback into the evaluation framework for a more accurate assessment.