Instruction Induction: From Few Examples to Natural Language Task Descriptions (2205.10782v1)

Abstract: LLMs are able to perform a task by conditioning on a few input-output demonstrations - a paradigm known as in-context learning. We show that LLMs can explicitly infer an underlying task from a few demonstrations by prompting them to generate a natural language instruction that fits the examples. To explore this ability, we introduce the instruction induction challenge, compile a dataset consisting of 24 tasks, and define a novel evaluation metric based on executing the generated instruction. We discover that, to a large extent, the ability to generate instructions does indeed emerge when using a model that is both large enough and aligned to follow instructions; InstructGPT achieves 65.7% of human performance in our execution-based metric, while the original GPT-3 model reaches only 9.8% of human performance. This surprising result suggests that instruction induction might be a viable learning paradigm in and of itself, where instead of fitting a set of latent continuous parameters to the data, one searches for the best description in the natural language hypothesis space.

• The paper introduces instruction induction, showing that LLMs can articulate natural language task descriptions from limited examples.
• It evaluates 24 diverse tasks using a novel execution accuracy metric, with InstructGPT achieving 65.7% of human performance compared to GPT-3’s 9.8%.
• The study underscores that large, instruction-tuned models enhance interpretability and mitigate overfitting, reshaping task specification in AI.

Instruction Induction: From Few Examples to Natural Language Task Descriptions

The paper, "Instruction Induction: From Few Examples to Natural Language Task Descriptions," presents a notable paper on the capability of LLMs to generate explicit task descriptions from limited input-output demonstrations, thereby extending the in-context learning paradigm. In-context learning, previously confined to the implicit task inference from few-shot examples, is expanded through instruction induction—prompting models to articulate the task in natural language. This exploration leverages the capabilities of InstructGPT, a model fine-tuned to follow instructions, revealing its superiority in harnessing instruction induction compared to its predecessor, GPT-3.

The researchers establish the instruction induction challenge, which serves to evaluate whether LLMs can infer and describe tasks like morphosyntactic conversions, sentiment analysis, and style transfer without fine-tuning. The paper compiles a dataset of 24 diverse tasks and introduces a novel evaluation metric—execution accuracy, assessing the model's ability to perform tasks solely based on the generated instructions. InstructGPT achieves 65.7% of human performance in execution accuracy, substantially outperforming GPT-3, which reaches only 9.8%.

The paper highlights that the potential of instruction induction becomes evident with models that are both large and aligned for instructions—demonstrated by InstructGPT's accomplishment of a BERTScore of 44.4, in contrast to GPT-3's significantly lower performance. Conversely, smaller models or those without specific instruction tuning exhibit negligible instruction induction abilities, underscoring the impact of model size and specialization in unlocking such capabilities. These findings resonate with current research indicating that scaling models enables new functionalities, and that instruction tuning significantly enhances their effective application.

A crucial contribution of this paper lies in the execution-based metric, allowing direct assessment of the correctness of model-generated instructions by task performance using such instructions. This empirical metric ensures robustness against superficial text overlaps, providing a more genuine reflection of the instructions' utility. Moreover, instruction induction proposes a paradigm shift, potentially transforming traditional methods of parameter tuning into natural language hypothesis exploration, which is inherently more interpretable to humans.

The research also considers broader implications, suggesting that empowering models to generate comprehensible task instructions offers the advantage of enhanced interpretability. Additionally, this could mitigate issues related to overfitting and spurious correlations which often plague data-driven model learning. Thus, grounding in natural language is posited as an advantageous strategy within machine learning, enhancing transparency and alignment with human understanding.

Future work could explore refining instruction induction with extended demonstration sets to resolve ambiguities, potentially increasing task complexity and accuracy. Additionally, further research into fine-tuning methodologies or alternative model architectures could amplify instruction induction capabilities, aligning performances even closer to human proficiency.

In conclusion, the paper structurally exemplifies the emerging capacity of LLMs to articulate task descriptions explicitly through instruction induction, propelled significantly by model size and specialized tuning. It occupies a pivotal intersection of in-context learning and natural language processing, promising substantive implications for future AI developments and practical applications in task specification and model interpretability. This shift not only broadens the horizons of intelligent model interactions but also reimagines foundational paradigms of machine learning.