SEE: Continual Fine-tuning with Sequential Ensemble of Experts (2025.findings-acl)
Copied to clipboard
| Challenge: | Continual fine-tuning of large language models suffers from catastrophic forgetting . some approaches use routers to assign tasks to experts, but continual learning often requires retraining . |
| Approach: | They propose a framework that integrates routing and response mechanisms within each expert . it eliminates the need for an additional router and allows each expert to decide whether a query should be handled . |
| Outcome: | The proposed framework outperforms previous approaches in continual fine-tuning . it can handle learning tasks and out-of-distribution instances, paving the way for distributed model ensembling. |
Similar Papers
Fine-tuned Language Models are Continual Learners (2022.emnlp-main)
Copied to clipboard
| Challenge: | Recent work on large language models relies on intuition that most tasks can be described via natural language instructions. |
| Approach: | They propose that a model should be able to keep extending its knowledge without forgetting previous skills. |
| Outcome: | The proposed model can learn 8 new diverse language generation tasks while maintaining good performance on previous tasks, spanning in total of 70 datasets. |
InsCL: A Data-efficient Continual Learning Paradigm for Fine-tuning Large Language Models with Instructions (2024.naacl-long)
Copied to clipboard
| Challenge: | In order to perform downstream tasks, Large Language Models (LLMs) need continual adaptation without catastrophic forgetting. |
| Approach: | They propose a new paradigm that allows for continual adaptation without catastrophic forgetting . they propose to replay previous data based on task similarity with instructions . |
| Outcome: | The proposed method improves performance over 16 tasks with different training orders. |
LPC: A Logits and Parameter Calibration Framework for Continual Learning (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to solve catastrophic forgetting problem are varied . current approaches to learn continuous learning are based on replay-based methods . |
| Approach: | They propose to calibrate parameters and logits so that preserving old parameters and generalized learning on new concepts can be solved simultaneously. |
| Outcome: | The proposed model achieves state-of-the-art performance in all scenarios. |
Rethinking Long Context Generation from the Continual Learning Perspective (2025.coling-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) struggle with processing long contexts due to the limited context window. |
| Approach: | They propose to combine a limited context window with a continual learning perspective to improve LLMs' efficiency in processing long contexts. |
| Outcome: | The proposed models improve the performance of Large Language Models (LLMs) by integrating learning strategies with existing approaches. |
Towards Anytime Fine-tuning: Continually Pre-trained Language Models with Hypernetwork Prompts (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Continual pre-training has been used for a multitude of domains and tasks . a continually pre-trained model can show a non-decreasing performance on unseen domains . |
| Approach: | They propose a method that generates domain-specific prompts by agreement and disagreement losses. |
| Outcome: | The proposed method achieves improvements of 3.57% and 3.4% on two real-world datasets. |
MoExtend: Tuning New Experts for Modality and Task Extension (2024.acl-srw)
Copied to clipboard
| Challenge: | Existing instruction tuning methods for large language models (LLMs) are costly and difficult to implement. |
| Approach: | They propose a framework to streamline the modality adaptation and extension of Mixture-of-Experts (MoE) models. |
| Outcome: | The proposed framework enables rapid adaptation and extension to new modal data or tasks without tuning pretrained models. |
Recall and Learn: Fine-tuning Deep Pretrained Language Models with Less Forgetting (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to fine-tune deep pretrained language models face catastrophic forgetting problems. |
| Approach: | They propose a recall and learn mechanism which integrates pretraining and downstream tasks into a single mechanism. |
| Outcome: | The proposed method achieves state-of-the-art performance on the GLUE benchmark and better average performance than directly fine-tuning of BERT-large. |
Recurrent Knowledge Identification and Fusion for Language Model Continual Learning (2025.acl-long)
Copied to clipboard
Yujie Feng, Xujia Wang, Zexin Lu, Shenghong Fu, Guangyuan Shi, Yongxin Xu, Yasha Wang, Philip S. Yu, Xu Chu, Xiao-Ming Wu
| Challenge: | Continual learning (CL) is crucial for large language models without costly retraining. |
| Approach: | They propose a framework for recurrent knowledge identification and fusion that enables dynamic estimation of parameter importance distributions to enhance knowledge transfer. |
| Outcome: | The proposed framework mitigates catastrophic forgetting and enhances knowledge transfer. |
HFT: Half Fine-Tuning for Large Language Models (2025.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) with one or more fine-tuning phases can unlock various capabilities, but can be catastrophic forgetting during sequential training. |
| Approach: | They propose a method to regularly reset partial parameters to mitigate forgetting issues by using half fine-tuning instead of full fine-uning. |
| Outcome: | The proposed approach reduces the risk of catastrophic forgetting during training and the parametric knowledge lost during training may be overwhelmed by incoming training data. |
Parameter-Efficient Finetuning for Robust Continual Multilingual Learning (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to Continual Multilingual Learning (CML) are based on updating models using new data in stages. |
| Approach: | They propose a parameter-efficient finetuning strategy to increase the number of languages on which the model improves after an update while reducing the magnitude of loss for the remaining languages. |
| Outcome: | The proposed model improves on the languages included in the latest update while reducing the loss of performance on the remaining languages. |