Large language models (LLMs) have transformed fields from customer service to healthcare by aligning machine output with human values. Reward models (RMs) play an important role in this alignment, as they essentially serve as a feedback loop where the models are guided to provide responses preferred by humans. While many advances have optimized these models for English, there is a broader challenge in adapting RMs to multilingual contexts. This adaptation is essential, given the global user base that increasingly relies on LLMs in multiple languages for a variety of tasks, including everyday information, safety guidelines, and nuanced conversations.
A central issue in LLM development lies in adapting RMs to perform consistently in different languages. Traditional reward models, trained primarily on English data, often need to catch up when extended to other languages. This limitation creates a performance gap that restricts the applicability of these models, particularly for non-English speaking users who rely on language models for accurate, culturally relevant, and safe answers. The current gap in RM capabilities underscores the need for multilingual benchmarks and evaluation tools to ensure models serve a global audience more effectively.
Existing evaluation tools, such as RewardBench, focus on evaluating English models for general capabilities such as reasoning, chat functionality, and user safety. While this benchmark has established a foundation for evaluating English-based RMs, it must address the multilingual dimensions necessary for broader applicability. RewardBench, as it stands, does not fully take into account tasks involving translation or cross-cultural responses. This highlights a critical area for improvement, as accurate translations and culturally aligned responses are critical to a meaningful user experience in different languages.
Researchers from Writesonic, Allen Institute of ai, Bangladesh University of Engineering and technology, ServiceNow, Cohere For ai Community, Cohere and Cohere For ai developed the M-RewardBencha new multilingual assessment benchmark designed to assess RMs across a spectrum of 23 languages. The dataset, spanning 2,870 preference instances, includes languages from eight unique scripts and multiple language families, providing a rigorous multilingual testing environment. M-RewardBench aims to close the RM assessment gap by covering languages from diverse typological backgrounds, providing new insights into how non-English LLMs perform in essential areas such as security, reasoning, chat capability and translation.
The M-RewardBench methodology comprehensively evaluates multilingual reward models, using machine-generated and human-verified translations to ensure accuracy. The researchers created subsets based on task difficulty and language complexity, translating and adapting RewardBench prompts into 23 languages. The benchmark includes Chat, Chat-Hard, Security, and Reasoning categories to evaluate RM capabilities in everyday and complex conversational environments. To measure the impact of translation quality, the research team used two translation systems, Google Translate and NLLB 3.3B, showing that improved translation can significantly improve RM performance by up to 3%.
The study revealed substantial disparities in performance, particularly between English and non-English contexts. Generative reward models, such as GPT-4-Turbo, performed relatively well, achieving an accuracy score of 83.5%, while other types of RM, such as classifier-based models, struggled with switching to multilingual tasks. The results indicate that generative models are better suited for multilingual alignment, although an average performance drop of 8% persists when moving from English to non-English tasks. Furthermore, the performance of the models varied significantly by language: languages with higher resources, such as Portuguese, achieved higher accuracy (68.7%) compared to languages with lower resources, such as Arabic (62.8% ).
Several key insights emerged from M-RewardBench that highlight areas for improvement in multilingual RM development. For example, RMs showed a higher degree of label consistency across languages for reasoning tasks than for general chat conversations, suggesting that certain types of content may be more adaptable to multilingual contexts. This idea points to the need for specialized benchmarks within M-RewardBench to evaluate different types of content, especially as the models expand to underrepresented languages with unique grammatical structures.
Key research findings:
- Scope of the data set: M-RewardBench spans 23 languages, eight language families, and 2,870 preference instances, making it one of the most comprehensive multilingual MR assessment tools available.
- Performance gaps: Generative RMs achieved higher average scores, with a significant 83.5% in multilingual environments, but overall performance dropped by up to 13% on non-English tasks.
- Task Specific Variations: Chat-Hard tasks showed the greatest performance degradation (5.96%), while reasoning tasks had the least, highlighting that task complexity affects MR accuracy across languages.
- Impact on translation quality: Higher quality translations improved RM accuracy by up to 3%, emphasizing the need for refined translation methods in multilingual contexts.
- Consistency in high-resource languages: The models performed better in high-resource languages (e.g., Portuguese, 68.7%) and showed consistency problems in low-resource languages, such as Arabic (62.8%).
- Reference contribution: M-RewardBench provides a new framework for evaluating LLMs in languages other than English, laying the foundation for future improvements in RM alignment across cultural and linguistic contexts.
In conclusion, the research behind M-RewardBench illustrates a critical need for language models to more closely align with human preferences across languages. By providing a benchmark tailored to multilingual contexts, this research lays the foundation for future improvements in reward modeling, especially in handling cultural nuances and ensuring language consistency. The findings reinforce the importance of developing RMs that reliably serve a global user base, where linguistic diversity and translation quality are critical to performance.
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