| Challenge: | Recent introduced instruction-paradigm empowers non-expert users to leverage NLP resources by defining a new task in natural language. |
| Approach: | They propose to define a task in natural language without creating task-specific datasets or building models. |
| Outcome: | The proposed model outperforms multitask learning models but is far from state-of-the-art task-specific models. |
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A Few More Examples May Be Worth Billions of Parameters (2022.findings-emnlp)
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| Challenge: | Recent work on few-shot learning for natural language tasks explores the dynamics of scaling up either the number of model parameters or labeled examples while controlling for the other variable by setting it to a constant. |
| Approach: | They explore the dynamics of scaling up the number of model parameters versus the number labeled examples across a wide variety of tasks. |
| Outcome: | The results show that scaling parameters yields performance improvements, while adding examples does not. |
Demystifying Instruction Mixing for Fine-tuning Large Language Models (2024.acl-srw)
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| Challenge: | Instruction tuning is effective for aligning large language models with human instructions, but the procedure to optimizing the mixing of instruction datasets is still unclear. |
| Approach: | They categorize instructions into three primary types: NLP downstream tasks, coding, and general chat. |
| Outcome: | The proposed method improves performance of large language models (LLMs) but it is difficult to combine different instruction datasets to optimize overall performance. |
Instruction Induction: From Few Examples to Natural Language Task Descriptions (2023.acl-long)
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| Challenge: | Large language models can perform unseen tasks by conditioning on a few input-output demonstrations, but task inference is implicit and the ability of models to explicitly reason about it remains unexplored. |
| Approach: | They propose an instruction induction challenge in which a model is asked to generate a natural language instruction that fits a set of labeled examples. |
| Outcome: | The proposed model achieves 65.7% of human performance while the original model only reaches 9.8% of human performances. |
Advancing Language Models through Instruction Tuning: Recent Progress and Challenges (2025.emnlp-tutorials)
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| Challenge: | tutorial addresses three critical questions within the field of instruction tuning: (1) What are the current focal points in instruction tuning research? (2) What are best practices in training an instruction-following model? (3) What new challenges have emerged? |
| Approach: | This tutorial presents a systematic overview of recent advances in instruction tuning. |
| Outcome: | The tutorial covers different stages in model training: supervised fine-tuning, preference optimization, and reinforcement learning. |
Learning Instructions with Unlabeled Data for Zero-Shot Cross-Task Generalization (2022.emnlp-main)
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| Challenge: | Recent studies have shown that instruction tuning is effective in instruction learning for unseen tasks, but it relies on a large amount of human-annotated samples, which restricts its generalization. |
| Approach: | They propose an instruction tuning technique which fine-tunes a pre-trained language model on a massive collection of tasks described via human-craft instructions and then tests its generalization ability on unseen tasks. |
| Outcome: | The proposed method improves IT performance versus labeled data and training tasks by constructing pseudo-labeled data from unlabele . data is used to build a model that can learn from human instructions for zero-shot generalization on unseen tasks. |
Do Models Really Learn to Follow Instructions? An Empirical Study of Instruction Tuning (2023.acl-short)
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| Challenge: | Recent studies on instruction tuning (IT) have achieved great performance with zero-shot generalizability to unseen tasks. |
| Approach: | They analyze how models utilize instructions during IT by comparing model training with altered vs. original instructions. |
| Outcome: | The proposed model outperforms naive models in low resource setting. |
Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor (2023.acl-long)
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| Challenge: | Instruction tuning enables pretrained language models to perform new tasks from inference-time natural language descriptions without human supervision. |
| Approach: | They propose to use a dataset of natural language instructions to generate large datasets with no human supervision. |
| Outcome: | The proposed dataset outperforms open-source models on various benchmarks, and is cost-effective. |
Instruction Matters: A Simple yet Effective Task Selection for Optimized Instruction Tuning of Specific Tasks (2024.emnlp-main)
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| Challenge: | Experimental results show that instruction tuning improves zero-shot generalization across various tasks and improves performance of specific tasks. |
| Approach: | They propose a task selection method that leverages instruction information alone to identify relevant tasks and optimize instruction tuning for specific tasks. |
| Outcome: | The proposed method is significantly more efficient than traditional approaches, which require complex measurements of pairwise transferability between tasks or the creation of data samples for the target task. |
LLM-driven Instruction Following: Progresses and Concerns (2023.emnlp-tutorial)
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| Challenge: | a tutorial on task instruction is aimed at researchers and practitioners interested in NLP generalization . labeled examples are unlikely to be available in large numbers or do not exist . |
| Approach: | This tutorial will examine the progress of natural language processing (NLP) using labeled examples. authors propose that task instructions act as a novel resource for supervision. |
| Outcome: | This tutorial aims to answer questions about instruction-driven NLP . it focuses on the use of task instructions in a low-shot scenario . |
Fine-Tuning Large Language Models with Sequential Instructions (2025.naacl-long)
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| Challenge: | Existing instruction-tuned models struggle to adhere to a query with multiple intentions, which impairs their performance when the completion of several tasks is demanded by a single command. |
| Approach: | They develop an automatic process that turns existing data into diverse and complex task chains and a new benchmark to evaluate a model’s ability to follow all the instructions in a sequence. |
| Outcome: | The proposed model can follow instructions better and deliver higher results in coding, maths, and open-ended generation. |