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#large language models Open access Aug 2026

Language Identification Loss Impact on mT5 Zero-Shot Cross-Lingual Performance

Intermediate-task training---fine-tuning a pretrained model on an intermediate task before fine-tuning again on the target task---often improves model performance substantially on language understanding tasks in monolingual English settings. We investigate whether English intermediate-task training is still helpful on non-English target tasks. Using nine intermediate language-understanding tasks, we evaluate intermediate-task transfer in a zero-shot cross-lingual setting on the XTREME benchmark. We see large improvements from intermediate training on the BUCC and Tatoeba sentence retrieval tas Research goal: How does adding a language identification loss during intermediate task training on XTREME-M affect mT5's zero-shot cross-lingual EXACT match performance compared to standard fine-tuning? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.2/10.

Assignee Research · 0 citations
#large language models Open access Aug 2026

Fine-tuning mT5 for Zero-shot Cross-lingual Transfer in XTREME-R Languages

Intermediate-task training---fine-tuning a pretrained model on an intermediate task before fine-tuning again on the target task---often improves model performance substantially on language understanding tasks in monolingual English settings. We investigate whether English intermediate-task training is still helpful on non-English target tasks. Using nine intermediate language-understanding tasks, we evaluate intermediate-task transfer in a zero-shot cross-lingual setting on the XTREME benchmark. We see large improvements from intermediate training on the BUCC and Tatoeba sentence retrieval tas Research goal: What is the effect of fine-tuning intermediate-task trained mT5 models on a mix of high-resource and low-resource languages from XTREME-R on zero-shot cross-lingual transfer performance, evaluated using accuracy and F1 metrics? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.3/10.

Assignee Research · 0 citations
#large language models Open access Aug 2026

Language Identification Loss Impact on mT5 Zero-Shot Cross-Lingual Performance

Intermediate-task training---fine-tuning a pretrained model on an intermediate task before fine-tuning again on the target task---often improves model performance substantially on language understanding tasks in monolingual English settings. We investigate whether English intermediate-task training is still helpful on non-English target tasks. Using nine intermediate language-understanding tasks, we evaluate intermediate-task transfer in a zero-shot cross-lingual setting on the XTREME benchmark. We see large improvements from intermediate training on the BUCC and Tatoeba sentence retrieval tas Research goal: How does adding a language identification loss during intermediate task training on XTREME-M affect mT5's zero-shot cross-lingual EXACT match performance compared to standard fine-tuning? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.2/10.

Assignee Research · 0 citations
#large language models Open access Aug 2026

Performance of English vs Multilingual Intermediate-Task Trained mT5 Models in Zero-Shot Cross-Lingual Transfer on XTREME-R

Intermediate-task training---fine-tuning a pretrained model on an intermediate task before fine-tuning again on the target task---often improves model performance substantially on language understanding tasks in monolingual English settings. We investigate whether English intermediate-task training is still helpful on non-English target tasks. Using nine intermediate language-understanding tasks, we evaluate intermediate-task transfer in a zero-shot cross-lingual setting on the XTREME benchmark. We see large improvements from intermediate training on the BUCC and Tatoeba sentence retrieval tas Research goal: How does the performance of English intermediate-task trained mT5 models compare to models trained with multilingual intermediate tasks in zero-shot cross-lingual transfer on XTREME-R, measured by accuracy and F1 scores? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.3/10.

Assignee Research · 0 citations
#large language models Open access Aug 2026

Fine-tuning mT5 for Zero-shot Cross-lingual Transfer in XTREME-R Languages

Intermediate-task training---fine-tuning a pretrained model on an intermediate task before fine-tuning again on the target task---often improves model performance substantially on language understanding tasks in monolingual English settings. We investigate whether English intermediate-task training is still helpful on non-English target tasks. Using nine intermediate language-understanding tasks, we evaluate intermediate-task transfer in a zero-shot cross-lingual setting on the XTREME benchmark. We see large improvements from intermediate training on the BUCC and Tatoeba sentence retrieval tas Research goal: What is the effect of fine-tuning intermediate-task trained mT5 models on a mix of high-resource and low-resource languages from XTREME-R on zero-shot cross-lingual transfer performance, evaluated using accuracy and F1 metrics? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.3/10.

Assignee Research · 0 citations
#large language models Open access Aug 2026

Inference Efficiency Trade-offs in Zero-shot Cross-lingual Transfer with mT5 Models

Intermediate-task training---fine-tuning a pretrained model on an intermediate task before fine-tuning again on the target task---often improves model performance substantially on language understanding tasks in monolingual English settings. We investigate whether English intermediate-task training is still helpful on non-English target tasks. Using nine intermediate language-understanding tasks, we evaluate intermediate-task transfer in a zero-shot cross-lingual setting on the XTREME benchmark. We see large improvements from intermediate training on the BUCC and Tatoeba sentence retrieval tas Research goal: What is the inference efficiency trade-off between English intermediate-task trained mT5 models and multilingual intermediate-task trained models in zero-shot cross-lingual transfer on XTREME-R, measured by throughput and accuracy/F1 scores? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.3/10.

Assignee Research · 0 citations
#large language models Open access Aug 2026

Adversarial Fine-Tuning Effects on Zero-Shot Cross-Lingual Robustness in XTREME-R

Intermediate-task training---fine-tuning a pretrained model on an intermediate task before fine-tuning again on the target task---often improves model performance substantially on language understanding tasks in monolingual English settings. We investigate whether English intermediate-task training is still helpful on non-English target tasks. Using nine intermediate language-understanding tasks, we evaluate intermediate-task transfer in a zero-shot cross-lingual setting on the XTREME benchmark. We see large improvements from intermediate training on the BUCC and Tatoeba sentence retrieval tas Research goal: How does adversarial fine-tuning on intermediate tasks impact the robustness of zero-shot cross-lingual transfer in XTREME-R, measured by accuracy on adversarially perturbed inputs? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.2/10.

Assignee Research · 0 citations
#large language models Open access Aug 2026

Adversarial Robustness in Zero-Shot Cross-Lingual Transfer Models Post-English QA Training

Intermediate-task training---fine-tuning a pretrained model on an intermediate task before fine-tuning again on the target task---often improves model performance substantially on language understanding tasks in monolingual English settings. We investigate whether English intermediate-task training is still helpful on non-English target tasks. Using nine intermediate language-understanding tasks, we evaluate intermediate-task transfer in a zero-shot cross-lingual setting on the XTREME benchmark. We see large improvements from intermediate training on the BUCC and Tatoeba sentence retrieval tas Research goal: How robust are zero-shot cross-lingual transfer models (e.g., XLM-R, mT5) to adversarial examples in target languages after intermediate training on English question-answering tasks, measured by accuracy degradation on perturbed XTREME-R test sets? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.3/10.

Assignee Research · 0 citations
#large language models Open access Aug 2026

Impact of Intermediate-Task Training Sequence Length on Zero-Shot Cross-Lingual Transfer Inference Latency in XTREME-R

Intermediate-task training---fine-tuning a pretrained model on an intermediate task before fine-tuning again on the target task---often improves model performance substantially on language understanding tasks in monolingual English settings. We investigate whether English intermediate-task training is still helpful on non-English target tasks. Using nine intermediate language-understanding tasks, we evaluate intermediate-task transfer in a zero-shot cross-lingual setting on the XTREME benchmark. We see large improvements from intermediate training on the BUCC and Tatoeba sentence retrieval tas Research goal: What is the impact of intermediate-task training sequence length (i.e., number of intermediate tasks) on inference latency during zero-shot cross-lingual transfer on XTREME-R while maintaining performance? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.0/10.

Assignee Research · 0 citations
#large language models Open access Aug 2026

Intermediate-Task Training and Multilingual Pretraining for Zero-Shot Cross-Lingual Transfer on XTREME-R

Intermediate-task training---fine-tuning a pretrained model on an intermediate task before fine-tuning again on the target task---often improves model performance substantially on language understanding tasks in monolingual English settings. We investigate whether English intermediate-task training is still helpful on non-English target tasks. Using nine intermediate language-understanding tasks, we evaluate intermediate-task transfer in a zero-shot cross-lingual setting on the XTREME benchmark. We see large improvements from intermediate training on the BUCC and Tatoeba sentence retrieval tas Research goal: How does the combination of intermediate-task training with multilingual pretraining affect the zero-shot cross-lingual transfer performance on XTREME-R compared to using only multilingual pretraining without intermediate tasks, quantified by accuracy on adversarial examples? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.2/10.

Assignee Research · 0 citations
#large language models Open access Aug 2026

Intermediate-Task Training Effects on Zero-Shot Cross-Lingual Model Inference Efficiency in XTREME-R

Intermediate-task training---fine-tuning a pretrained model on an intermediate task before fine-tuning again on the target task---often improves model performance substantially on language understanding tasks in monolingual English settings. We investigate whether English intermediate-task training is still helpful on non-English target tasks. Using nine intermediate language-understanding tasks, we evaluate intermediate-task transfer in a zero-shot cross-lingual setting on the XTREME benchmark. We see large improvements from intermediate training on the BUCC and Tatoeba sentence retrieval tas Research goal: Does intermediate-task training improve the inference efficiency (measured in tokens/sec or latency) of zero-shot cross-lingual models on XTREME-R when evaluated on low-resource languages with varying target task data sizes? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.0/10.

Assignee Research · 0 citations
#large language models Open access Aug 2026

Adversarial Robustness in Zero-Shot Cross-Lingual Transfer Models Post-English QA Training

Intermediate-task training---fine-tuning a pretrained model on an intermediate task before fine-tuning again on the target task---often improves model performance substantially on language understanding tasks in monolingual English settings. We investigate whether English intermediate-task training is still helpful on non-English target tasks. Using nine intermediate language-understanding tasks, we evaluate intermediate-task transfer in a zero-shot cross-lingual setting on the XTREME benchmark. We see large improvements from intermediate training on the BUCC and Tatoeba sentence retrieval tas Research goal: How robust are zero-shot cross-lingual transfer models (e.g., XLM-R, mT5) to adversarial examples in target languages after intermediate training on English question-answering tasks, measured by accuracy degradation on perturbed XTREME-R test sets? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.3/10.

Assignee Research · 0 citations