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Original Paper
Original Paper: https://arxiv.org/abs/2303.10475
Abstract:
Task semantics can be expressed by a set of input-output examples or a piece of textual instruction. Conventional machine learning approaches for natural language processing (NLP) mainly rely on the availability of large-scale sets of task-specific examples. Two issues arise: first, collecting task-specific labeled examples does not apply to scenarios where tasks may be too complicated or costly to annotate, or the system is required to handle a new task immediately; second, this is not user-friendly since end-users are probably more willing to provide task description rather than a set of examples before using the system. Therefore, the community is paying increasing interest in a new supervision-seeking paradigm for NLP: learning to follow task instructions, i.e., instruction following. Despite its impressive progress, there are some common issues that the community struggles with. This survey paper tries to summarize and provide insights to the current research on instruction following, particularly, by answering the following questions: (i) What is task instruction, and what instruction types exist? (ii) How to model instructions? (iii) What are popular instruction following datasets and evaluation metrics? (iv) What factors influence and explain the instructions' performance? (v) What challenges remain in instruction following? To our knowledge, this is the first comprehensive survey about instruction following.
Summary Notes
Exploring Instruction Following in NLP: A Deep Dive
The realm of Natural Language Processing (NLP) is constantly advancing, and instruction following has become a key area of focus.
This approach, which differs from traditional example-driven learning, aims to understand and carry out complex instructions given in natural language, paving the way for AI systems that are more intuitive, adaptive, and efficient.
In this blog post, we explore the nuances of instruction following in NLP, including its workings, applications, and the hurdles it encounters.
Understanding Instruction Following
At its core, instruction following in NLP goes beyond mere prompt interpretation. It requires models to grasp and act on complex directives via indirect supervision. This shift towards instruction-based modeling is crucial for developing AI that can interact fluidly with human language across a wide range of tasks without needing constant retraining or explicit coding.
Types of Instructions
Instructions fall into three primary categories, each presenting distinct challenges:
- NLI-oriented Instructions: Focused on logical reasoning within text, akin to natural language inference tasks.
- LLM-oriented Instructions: Designed for Large Language Models to leverage their extensive knowledge and processing power for more abstract tasks.
- Human-oriented Instructions: The most complex category, requiring an in-depth understanding of context and implicit knowledge, tailored to human intuition.
Human-oriented instructions highlight the need for sophisticated modeling techniques due to their complexity and reliance on implicit knowledge.
Key Datasets
Various datasets have been developed to aid in training and assessing instruction-following models. These datasets are pivotal for benchmarking and advancing NLP models capable of executing a broad array of instructions. However, creating these datasets poses challenges, such as ensuring they are diverse, complex, and relevant to real-life tasks.
Evaluation and Performance
Evaluating these models necessitates metrics that accurately reflect their ability to comprehend and follow instructions. Success factors include task completion rate, accuracy, and generalization across instruction types. Crucially, success in instruction following benefits from dual-track scaling, enhancing both the models (especially LLMs) and the instruction datasets, ensuring models are continually tested and improved.
Challenges and Future Directions
Instruction following in NLP faces several obstacles, such as:
- Learning Negated Information: Grasping and applying instructions that involve negation.
- Adversarial Instruction Attacks: Handling instructions designed to mislead or confuse the models.
- Explainability: Offering clear justifications for model actions in response to instructions.
Going forward, the aim is to not only refine models' instruction-following capabilities but also improve their learning efficiency from these instructions, moving closer to achieving artificial general intelligence in NLP.
Applications
Instruction following has wide-ranging applications, such as:
- Human-Computer Interaction: Enhancing natural language interactions between humans and AI.
- Data and Feature Augmentation: Innovatively generating or modifying data and features via instructions.
- Developing Generalist Language Models: Crafting models capable of handling various tasks based on instructions alone, without task-specific training.
These applications underscore the potential of instruction following to achieve cross-task generalization, a key step towards realizing artificial general intelligence in NLP.
Conclusion
The field of instruction following in NLP is setting the stage for AI systems that can accurately interpret and act on complex human instructions. Despite facing numerous challenges, the potential benefits, from improving human-computer interactions to creating generalist language models, are substantial.
As we delve deeper into this area, the insights and advancements from instruction following will undoubtedly shape the future of NLP and AI, offering new opportunities for innovation and efficiency in AI solutions.
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