Papers with re-training models
Evolutionary Guided Decoding: Iterative Value Refinement for LLMs (2026.acl-long)
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| Challenge: | Existing methods for directing language model outputs are limited in their accuracy due to a distributional gap . existing methods train static value functions on trajectories sampled exclusively from the base policy . |
| Approach: | They propose a framework to bridge a distributional gap in the accuracy of value functions . they propose RLHF to align language models with human values and task requirements . |
| Outcome: | The proposed framework reduces computational costs and improves value function accuracy by leveraging principled value function optimization. |
Total Recall: a Customized Continual Learning Method for Neural Semantic Parsers (2021.emnlp-main)
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| Challenge: | Existing methods for continual learning for semantic parsing fail to account for special properties of structured outputs . retraining from scratch is not feasible due to the fast growing number of tasks . |
| Approach: | They propose a continual learning method that uses sequential learning to learn tasks without accessing full training data from previous tasks. |
| Outcome: | The proposed method achieves a 3-6 times speedup compared to re-training from scratch. |
Contrastive Error Attribution for Finetuned Language Models (2023.acl-long)
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| Challenge: | Existing methods for error tracing do not detect faithfulness errors in NLG datasets. |
| Approach: | They propose a framework to identify and remove low-quality training instances that lead to undesirable outputs. |
| Outcome: | The proposed method outperforms existing methods for detecting faithfulness errors in NLG datasets. |