Learning to Instruct: Fine-Tuning a Task-Aware Instruction Optimizer for Black-Box LLMs (2025.findings-emnlp)
Copied to clipboard
Yunzhe Qi, Jinjin Tian, Tianci Liu, Ruirui Li, Tianxin Wei, Hui Liu, Xianfeng Tang, Monica Xiao Cheng, Jingrui He
| Challenge: | Learning to Instruct is a new paradigm for black-box LLMs with inaccessible internal states. |
| Approach: | They propose a new paradigm that formulates instruction optimization as an LLM fine-tuning objective for a white-box “instruction engineer” LLM. |
| Outcome: | The proposed framework outperforms strong baselines in performance and efficiency. |
Similar Papers
Auto-Instruct: Automatic Instruction Generation and Ranking for Black-Box Language Models (2023.findings-emnlp)
Copied to clipboard
Zhihan Zhang, Shuohang Wang, Wenhao Yu, Yichong Xu, Dan Iter, Qingkai Zeng, Yang Liu, Chenguang Zhu, Meng Jiang
| Challenge: | Large language models can perform a wide range of tasks by following natural language instructions without task-specific fine-tuning. |
| Approach: | They propose a method to automatically improve the quality of LLM instructions . they leverage the generative ability of LMS to generate diverse candidate instructions based on a scoring model trained on 575 existing NLP tasks. |
| Outcome: | The proposed method surpasses human-written and LLM-generated instructions on 118 out-of-domain tasks. |
ACING: Actor-Critic for Instruction Learning in Black-Box LLMs (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have impressive capabilities across tasks like classification, summarization, and reasoning. |
| Approach: | They propose an actor-critic reinforcement learning framework that formulates instruction optimization as a stateless, continuous-action problem. |
| Outcome: | The proposed framework outperforms human-written prompts in 76% of instruction-induction tasks with gains of 33 points and 10-point improvement over baseline. |
Self-Instruct: Aligning Language Models with Self-Generated Instructions (2023.acl-long)
Copied to clipboard
Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, Hannaneh Hajishirzi
| Challenge: | Large “instruction-tuned” language models depend heavily on human-written instruction data . this limited quantity, diversity, and creativity hinders the generality of the tuned model . |
| Approach: | They propose a framework for improving instruction-following capabilities of pretrained language models by bootstrapping off their own generations. |
| Outcome: | The proposed framework outperforms existing public instruction datasets by 5% . it generates instructions, input, and output samples, then filters invalid or similar ones . |
Ada-Instruct: Adapting Instruction Generators for Complex Reasoning (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for generating instructions from a few initial samples with in-context learning are lacking in generating complex instructions of length 100. |
| Approach: | They propose an adaptive instruction generator developed through fine-tuning that generates long, intricate, and distributionally consistent instructions. |
| Outcome: | The proposed method generates long, intricate, and distributionally consistent instructions with ten examples. |
ILLUMINER: Instruction-tuned Large Language Models as Few-shot Intent Classifier and Slot Filler (2024.lrec-main)
Copied to clipboard
| Challenge: | State-of-the-art intent classification and slot filling methods rely on data-intensive deep learning models . large language models exhibit remarkable zero-shot performance across various natural language tasks. |
| Approach: | They propose an approach framing IC and SF as language generation tasks for instruction-LLMs with a more efficient SF-prompting method. |
| Outcome: | The proposed approach outperforms state-of-the-art IC+SF method and in-context learning methods with GPT3.5 (175B). |
Ensemble-Instruct: Instruction Tuning Data Generation with a Heterogeneous Mixture of LMs (2023.findings-emnlp)
Copied to clipboard
Young-Suk Lee, Md Sultan, Yousef El-Kurdi, Tahira Naseem, Asim Munawar, Radu Florian, Salim Roukos, Ramón Astudillo
| Challenge: | Empirical studies with different instruction-tuned LMs show that our proposed method yields higher-quality instruction tuning data than Self-Instruct. |
| Approach: | They propose to use in-context learning techniques to train strong conversational agents . they propose to categorize and simplify ICL templates to make prompt learning easier . |
| Outcome: | Empirical results show that the proposed method yields higher-quality instruction tuning data than Self-Instruct and improves performance of both vanilla and instruction-tuned LMs. |
Revealing the Inherent Instructability of Pre-Trained Language Models (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Pre-trained large language models perform multitask learning during their pre-training . a new technique, Response Tuning, removes the instruction and its corresponding mapping to the response from instruction tuning. |
| Approach: | They propose a method which removes the instruction and its mapping to the response from instruction tuning. |
| Outcome: | The proposed model can respond to a wide range of instructions . it can recognize and reject unsafe queries after learning from response data. |
EasyInstruct: An Easy-to-use Instruction Processing Framework for Large Language Models (2024.acl-demos)
Copied to clipboard
Yixin Ou, Ningyu Zhang, Honghao Gui, Ziwen Xu, Shuofei Qiao, Runnan Fang, Lei Li, Zhen Bi, Guozhou Zheng, Huajun Chen
| Challenge: | Large Language Models (LLMs) have improved performance across tasks and domains . instruction tuning is a crucial technique to enhance the capabilities of LLMs - but there is no standard open-source instruction processing framework available for the community . |
| Approach: | They propose an open-source instruction tuning framework for Large Language Models that modularizes instruction generation, selection, prompting and their combination and interaction. |
| Outcome: | The proposed framework is open-source and available on Github. |
Revisiting Instruction Fine-tuned Model Evaluation to Guide Industrial Applications (2023.emnlp-main)
Copied to clipboard
| Challenge: | Instruction fine-tuned (IFT) models are gaining traction in industrial NLP to unlock task-specific performance gains and strengthen model alignment with industry requirements. |
| Approach: | They propose to use instruction fine-tuned (IFT) models to enhance the zero-shot capabilities of Large Language Models (LLMs) they also propose to leverage IFT models to analyze the trade-offs that emerge in industrial settings. |
| Outcome: | The proposed model is well adapted to new evaluation metric requirements, and offers practical insights for real-world LLM deployment. |
DS2-Instruct: Domain-Specific Data Synthesis for Large Language Models Instruction Tuning (2026.findings-eacl)
Copied to clipboard
| Challenge: | Existing data synthesis methods focus on general-purpose tasks and fail to capture domain-specific terminology and reasoning patterns. |
| Approach: | They propose a framework that generates domain-specific instruction datasets without human supervision by pairing task-informed keywords with different cognitive levels from Bloom’s Taxonomy. |
| Outcome: | The proposed framework generates domain-specific instruction datasets without human supervision and achieves significant improvements over existing methods. |