Papers with multilingual generation
Evaluating the Impact of SAE-based Language Steering on LLM Performance (2026.eacl-srw)
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| Challenge: | Recent advances in Sparse Autoencoders (SAEs) have revealed interpretable features within large language models (LLMs) however, the impact of SAE-based language steering on output quality and task performance remains unclear. |
| Approach: | They apply language-specific SAE feature steering to three LLMs from two model families and evaluate it on a translation task and a multilingual question-answering task. |
| Outcome: | The proposed approach outperforms prompting and language neuron-based steering on translation and multilingual question-answering tasks. |
Language on Demand, Knowledge at Core: Composing LLMs with Encoder-Decoder Translation Models for Extensible Multilinguality (2026.acl-long)
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| Challenge: | Large language models exhibit strong general intelligence, yet their multilingual performance remains imbalanced. |
| Approach: | They propose a compositional encoder-LLM-decoder architecture that offloads multilingual understanding to external pretrained translation models while preserving the LLM as an English-centric core for general knowledge processing and reasoning. |
| Outcome: | The proposed architecture outperforms baseline models on four large language models across understanding, reasoning, and generation. |
Multilingual Generation in Abstractive Summarization: A Comparative Study (2024.lrec-main)
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| Challenge: | Existing models for multilingual generation lack thorough analysis due to extensive linguistic diversity. |
| Approach: | They propose to classify multilingual generation methodologies into three categories based on their underlying modeling principles . they introduce an automatic metric to mitigate spurious correlations associated with language mixing . |
| Outcome: | The proposed model improves in high-resource, low-resourced, and zero-shot scenarios. |
SNaRe: Domain-aware Data Generation for Low-Resource Event Detection (2025.emnlp-main)
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| Challenge: | Existing methods for ED struggle with label noise and domain drift when applied to specialized domains. |
| Approach: | They propose a domain-aware synthetic data generation framework composed of three components: Scout, Narrator, and Refiner. |
| Outcome: | The proposed framework outperforms baseline approaches on three diverse domain ED datasets and achieves average F1 gains of 3-7% in the zero-shot/few-shot settings and 4-20% improvement for multilingual generation. |
MUG-Eval: A Proxy Evaluation Framework for Multilingual Generation Capabilities in Any Language (2025.findings-emnlp)
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| Challenge: | Evaluating text generation capabilities of large language models (LLMs) is challenging, especially for low-resource languages where methods for direct assessment are scarce. |
| Approach: | They propose a framework that transforms existing benchmarks into conversational tasks and measures LLMs’ accuracies on those tasks. |
| Outcome: | The proposed framework correlates strongly with established benchmarks while enabling standardized comparisons across languages and models. |