An Incomplete Loop: Instruction Inference, Instruction Following, and In-context Learning in Language Models
This reveals non-systematic reasoning in large language models, highlighting that similar prompting procedures invoke different learning mechanisms, which is incremental for understanding model behavior.
The study investigated the relationship between instruction following, few-shot prompting, and instruction inference in language models, finding a strong dissociation where models can learn from few-shot prompts without explaining their rules and infer task descriptions while failing with human-generated ones.
Modern language models (LMs) can learn to perform new tasks in different ways: in instruction following, the target task is described explicitly in natural language; in few-shot prompting, the task is specified implicitly with a small number of examples; in instruction inference, LMs are presented with in-context examples and are then prompted to generate a natural language task description before making predictions. Each of these procedures may be thought of as invoking a different form of reasoning: instruction following involves deductive reasoning, few-shot prompting involves inductive reasoning, and instruction inference involves abductive reasoning. How do these different capabilities relate? Across four LMs (from the gpt and llama families) and two learning problems (involving arithmetic functions and machine translation) we find a strong dissociation between the different types of reasoning: LMs can sometimes learn effectively from few-shot prompts even when they are unable to explain their own prediction rules; conversely, they sometimes infer useful task descriptions while completely failing to learn from human-generated descriptions of the same task. Our results highlight the non-systematic nature of reasoning even in some of today's largest LMs, and underscore the fact that very different learning mechanisms may be invoked by seemingly similar prompting procedures.