Papers with adaptation strategies
Domain Differential Adaptation for Neural Machine Translation (D19-56)
Copied to clipboard
| Challenge: | Neural networks are data hungry and domain sensitive, so it is difficult to obtain labeled data for every domain. |
| Approach: | They propose a framework for domain adaptation where we model the difference between domains instead of smoothing over them. |
| Outcome: | The proposed framework improves on domain adaptation in multiple experimental settings. |
Tailored Emotional LLM-Supporter: Enhancing Cultural Sensitivity (2026.eacl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) have shown growing potential in offering emotional support, but their ability to deliver culturally sensitive support remains underexplored due to a lack of resources. |
| Approach: | They propose a large language model dataset that includes 1,729 distress messages, 1,523 cultural signals and 1,041 support strategies with fine-grained emotional and cultural annotations. |
| Outcome: | The proposed models outperform peer-reviewed models and lack cultural sensitivity. |
Thesis Proposal: Diagnosing and Mitigating Semantic Interference in Script-Sharing Low-Resource Language Models: A Case Study on Square Bai Script (2026.acl-srw)
Copied to clipboard
| Challenge: | Multilingual models cover more languages than ever, yet script-sharing low-resource languages remain vulnerable to failures driven by script and dominant-language priors. |
| Approach: | They propose to diagnose semantic interference in Square Bai Script, a script-sharing low-resource language . they will compare adaptation strategies under realistic compute constraints and estimate when shared-script transfer helps or harms adaptation. |
| Outcome: | The proposed model underperforms in a script-sharing low-resource setting because Bai is low-source and because it treats visible overlap as safe transfer by default. |
MELT: Materials-aware Continued Pre-training for Language Model Adaptation to Materials Science (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods focused on constructing domain-specific corpus focus on a limited and scarce nature of datasets in materials science poses significant challenges for developing models that generalize well across a broad range of materials entities. |
| Approach: | They propose a method to adapt pre-trained language models for materials science by continuously pre-training them on a materials science corpus. |
| Outcome: | The proposed method is able to adapt pre-trained language models for materials science tasks. |
Asymmetric Conflict and Synergy in Post-training for LLM-based Multilingual Machine Translation (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing work in LLM-based MMT typically mitigates the Curse of Multilinguality . asymmetric phenomenon in linguistic conflicts and synergy varies in different translation directions . |
| Approach: | They propose a direction-aware training approach to address asymmetry in linguistic conflicts and synergy . they propose X-ALMA-13B-Pretrain with multilingual pre-training to achieve comparable performance . |
| Outcome: | The proposed method achieves comparable performance to X-ALMA-13B-Pretrain (only SFT) with fewer pretraining tokens and 17B parameters. |