Papers with external model
Rethinking Data Selection at Scale: Random Selection is Almost All You Need (2025.findings-emnlp)
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| Challenge: | Existing data selection techniques are designed for small data pools, a study finds . filtering data by token length is an efficient method for improving results . |
| Approach: | They use self-scoring methods that do not rely on external help to perform fine-tuning . they also find that filtering data by token length offers a stable and efficient method . |
| Outcome: | The proposed methods outperform random selection on large datasets on large data pools. |
PREADD: Prefix-Adaptive Decoding for Controlled Text Generation (2023.findings-acl)
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| Challenge: | Existing methods for controlling text generation rely on auxiliary expert models but require training data. |
| Approach: | They propose a flexible method for controlled text generation that uses a prefix-prepended prompt instead of an external model. |
| Outcome: | The proposed method outperforms auxiliary-expert methods on three tasks by 12% or more on main metrics for each task. |
Knowledge Transfer in Incremental Learning for Multilingual Neural Machine Translation (2023.acl-long)
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| Challenge: | Existing studies focus on overcoming catastrophic forgetting on original language pairs while lacking encouragement to learn new knowledge from incremental learning. |
| Approach: | They propose a knowledge transfer method that can adapt original MNMT models to diverse incremental language pairs by flexibly introducing knowledge from external models into original models, which encourages the models to learn new language pairs. |
| Outcome: | The proposed method outperforms baselines on multiple languages while maintaining performance on original language pairs. |
Priority on High-Quality: Selecting Instruction Data via Consistency Verification of Noise Injection (2025.emnlp-main)
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| Challenge: | Existing methods for instruction selection rely on external models or rules, overlooking the intrinsic association between pre-trained model and instruction data. |
| Approach: | They propose a method that utilizes noise injection to identify the quality of instruction data without relying on external models. |
| Outcome: | The proposed method outperforms the model trained on the entire dataset and established baselines. |
Controlling What You Share: Assessing Language Model Adherence to Privacy Preferences (2026.findings-acl)
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| Challenge: | Large language models (LLMs) are accessed via commercial APIs, but expose data to service providers. |
| Approach: | They propose a framework where a local model uses natural language instructions to rewrite queries and paired them with synthetic privacy profiles to achieve better privacy preservation. |
| Outcome: | The proposed model outperforms large-scale few-shot models in terms of privacy preservation and performance. |