Towards Typologically Aware Rescoring to Mitigate Unfaithfulness in Lower-Resource Languages
This addresses unfaithfulness in lower-resource languages for NLP applications, but it is incremental as it builds on existing rescoring methods with typological insights.
The paper tackled the problem of multilingual large language models generating non-faithful outputs in lower-resource languages by proposing a computationally light rescoring approach using auxiliary models, achieving a mean accuracy of 88.33% in identifying faithful summaries across three typologically diverse languages.
Multilingual large language models (LLMs) are known to more frequently generate non-faithful output in resource-constrained languages (Guerreiro et al., 2023 - arXiv:2303.16104), potentially because these typologically diverse languages are underrepresented in their training data. To mitigate unfaithfulness in such settings, we propose using computationally light auxiliary models to rescore the outputs of larger architectures. As proof of the feasibility of such an approach, we show that monolingual 4-layer BERT models pretrained from scratch on less than 700 MB of data without fine-tuning are able to identify faithful summaries with a mean accuracy of 88.33% in three genetically unrelated languages that differ in their morphological complexity - Vietnamese, Polish and Georgian. The same hyperparameter combination moreover generalises well to three other tasks, suggesting applications for rescoring beyond improving faithfulness. In order to inform typologically aware model selection, we also investigate how morphological complexity interacts with regularisation, model depth and training objectives, ultimately demonstrating that morphologically complex languages are more likely to benefit from dropout, while across languages downstream performance is enhanced most by shallow architectures as well as training using the standard BERT objectives.