Papers with tuning methods
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. |
A Simple-Yet-Efficient Instruction Augmentation Method for Zero-Shot Sentiment Classification (2025.coling-main)
Copied to clipboard
| Challenge: | Existing studies have used labeled sentiment instances to instruction tune LLMs, improving zero-shot sentiment classification performance. |
| Approach: | They propose a simple-yet-efficient method which does not rely on actual labeled sentiment instances. |
| Outcome: | The proposed method outperforms LLMs tuned with more complex instruction tuning methods by 5.1 points and increases scores by 30 points. |
Dialogue is Better Than Monologue: Instructing Meidcal LLMs via Strategic Conversations (2026.findings-eacl)
Copied to clipboard
Zijie Liu, Xinyu Zhao, Jie Peng, Jinhao Duan, Zhuangdi Zhu, Qingyu Chen, Kaidi Xu, Xia Hu, Tianlong Chen
| Challenge: | Existing tuning methods for medical AI models are monologue-based . existing benchmarks are based on licensing exams or research articles . |
| Approach: | They propose a benchmark to expose limitations of monologue-based tuning for medical AI models . they use a large dialogue dataset to capture stepwise diagnostic reasoning . |
| Outcome: | The proposed model outperforms monologue-tuned models on a medical question answering task and improves accuracy on standard medical QA benchmarks. |
On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation (2021.acl-long)
Copied to clipboard
Ruidan He, Linlin Liu, Hai Ye, Qingyu Tan, Bosheng Ding, Liying Cheng, Jiawei Low, Lidong Bing, Luo Si
| Challenge: | Existing studies have shown that adapter-based tuning is more parameter-efficient than fine-tuning. |
| Approach: | They propose to add adapter modules to a pretrained language model and update the parameters of adapter module when learning on a downstream task. |
| Outcome: | The proposed method outperforms fine-tuning on low-resource and cross-lingual tasks and settings. |
AdaRewriter: Unleashing the Power of Prompting-based Conversational Query Reformulation via Test-Time Adaptation (2025.emnlp-main)
Copied to clipboard
| Challenge: | Prompting-based conversational query reformulation has emerged as a powerful approach for conversational search, refining ambiguous user queries into standalone search queries. |
| Approach: | They propose a framework for query reformulation using an outcome-supervised reward model via test-time adaptation. |
| Outcome: | Experiments on five conversational search datasets show that AdaRewriter significantly outperforms the existing methods across most settings. |
WaveCoder: Widespread And Versatile Enhancement For Code Large Language Models By Instruction Tuning (2024.acl-long)
Copied to clipboard
| Challenge: | Recent work shows that Code Large Language Models can address a wide range of code-related tasks. |
| Approach: | They propose a method to generate widespread and versatile instruction data from open source code datasets and use it to train code-related models. |
| Outcome: | The proposed model outperforms open-source models in generalization ability across code-related tasks. |
Efficiently Tuned Parameters Are Task Embeddings (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for intermediate-task transfer are computationally infeasible to experiment with all intermediate combinations. |
| Approach: | They propose to use task-specific parameters updated in parameter-efficient tuning methods to predict inter-task transferability. |
| Outcome: | The proposed approach outperforms existing methods while being conceptually simple and computationally efficient. |
R-Tuning: Instructing Large Language Models to Say ‘I Don’t Know’ (2024.naacl-long)
Copied to clipboard
Hanning Zhang, Shizhe Diao, Yong Lin, Yi Fung, Qing Lian, Xingyao Wang, Yangyi Chen, Heng Ji, Tong Zhang
| Challenge: | Existing methods for instruction tuning force the model to complete a sentence no matter whether it knows the knowledge or not. |
| Approach: | They propose a new approach to tuning large language models to refrain from answering questions beyond its parametric knowledge by identifying the disparity in parametric and parametric information. |
| Outcome: | The proposed approach improves a model’s ability to answer known questions and refrain from answering unknown questions. |
Residual Prompt Tuning: improving prompt tuning with residual reparameterization (2023.findings-acl)
Copied to clipboard
| Challenge: | Prompt tuning is one of the most parameter-efficient approaches for parameter-effective tuning of pre-trained language models. |
| Approach: | They propose to reparameterize soft prompt embeddings using a shallow network with a residual connection and use it to tune prompt embeds P. |
| Outcome: | The proposed method outperforms prompt tuning on SuperGLUE, T5-Base and BERT-Bass models and can reduce the prompt length by 10 times without hurting performance. |
Neural Network Surgery: Injecting Data Patterns into Pre-trained Models with Minimal Instance-wise Side Effects (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing neural network tuning methods cause instance-wise side effects . et al., 2018: a new approach to perform neural network surgery . |
| Approach: | They propose to perform neural network surgery by only changing 10-5 parameters . they propose to use a dynamic selecting method to achieve the best overall performance . |
| Outcome: | The proposed method achieves the best overall performance and induces fewer instance-wise side effects by changing only 10-5 of the parameters. |
ALIS: Aligned LLM Instruction Security Strategy for Unsafe Input Prompt (2025.coling-main)
Copied to clipboard
| Challenge: | Existing instruction tuning methods may fail to balance performance with robustness against attacks from user input like prompt injection and jailbreaking. |
| Approach: | They propose an instruction tuning paradigm to decompose user inputs into irreducible atomic instructions and organize them into instruction streams to guide response generation of model. |
| Outcome: | The proposed model can maintain security constraints by ignoring or rejecting user mode instructions when user mode instruction conflicts with kernel mode instructions. |
Exploring the Impact of Model Scaling on Parameter-Efficient Tuning (2023.emnlp-main)
Copied to clipboard
Yusheng Su, Chi-Min Chan, Jiali Cheng, Yujia Qin, Yankai Lin, Shengding Hu, Zonghan Yang, Ning Ding, Xingzhi Sun, Guotong Xie, Zhiyuan Liu, Maosong Sun
| Challenge: | Parameter-efficient tuning (PET) methods can drive large pre-trained language models by training only minimal parameters. |
| Approach: | They propose a parameter-efficient tuning method that is compatible with a tunable module and uses a random number generator to optimize fewer table parameters. |
| Outcome: | The proposed method is compatible with a tunable module and tested on 11 NLP tasks. |