Papers with tuning framework
InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning (2022.emnlp-main)
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| Challenge: | Instruction tuning is emerging in NLP, but has not been explored for dialogue-related tasks. |
| Approach: | They propose an instruction tuning framework for dialogue that leverages natural language instructions with language models to induce zero-shot generalization on unseen tasks. |
| Outcome: | The proposed framework enables good zero-shot performance on unseen datasets and tasks such as dialogue evaluation and intent detection. |
Retrieval-Augmented Process Reward Model for Generalizable Mathematical Reasoning (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have advanced mathematical reasoning, but they still struggle with out-of-distribution (OOD) issues. |
| Approach: | They propose a framework to evaluate the logical validity of reasoning steps . they retrieves semantically similar questions and steps for PRM as a warmup . |
| Outcome: | The proposed framework outperforms baseline models on multiple real-world datasets. |
X-Eval: Generalizable Multi-aspect Text Evaluation via Augmented Instruction Tuning with Auxiliary Evaluation Aspects (2024.naacl-long)
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| Challenge: | X-Eval is a two-stage instruction tuning framework to evaluate text in both seen and unseen aspects customized by end users. |
| Approach: | They introduce a two-stage instruction tuning framework to evaluate text in both seen and unseen aspects customized by end users. |
| Outcome: | The proposed framework improves the model’s ability to follow evaluation instructions and enhances the learning stage to better assess text quality. |
Matryoshka-Adaptor: Unsupervised and Supervised Tuning for Smaller Embedding Dimensions (2024.emnlp-main)
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| Challenge: | Embeddings from Large Language Models (LLMs) have emerged as critical components in information retrieval applications. |
| Approach: | They propose a tuning framework for the customization of LLM embeddings. |
| Outcome: | The proposed framework reduces embedding dimensions while maintaining comparable performance levels. |
FedDQC: Data Quality Control in Federated Instruction-tuning of Large Language Models (2025.findings-acl)
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| Challenge: | Federated Learning (FL) enables privacy-preserving collaborative instruction tuning of large language models. |
| Approach: | They propose a federated instruction tuning framework with dynamic data quality control to solve this problem. |
| Outcome: | The proposed framework improves performance on mixed-quality datasets on synthetic and real-world datasets. |
Error-driven Data-efficient Large Multimodal Model Tuning (2025.acl-long)
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| Challenge: | Large Multimodal Models (LMMs) have demonstrated impressive performance across numerous academic benchmarks, but task-specific tuning samples are often not readily available or expensive and time-consuming to obtain. |
| Approach: | They propose an error-driven data-efficient tuning framework that aims to efficiently adapt generic LMMs to newly emerging tasks without extensive task-specific training samples. |
| Outcome: | The proposed model achieves an average performance boost of 7.01% on seven tasks across three training data scales and three different training datascales. |