An Incomplete Loop: Instruction Inference, Instruction Following, and In-context Learning in Language Models (2404.03028v3)

Abstract: Modern LLMs (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.

• The paper demonstrates that instruction inference boosts performance in synthetic tasks such as arithmetic and artificial language translation.
• The paper reveals a disconnect between abductive reasoning used in instruction inference and inductive reasoning in few-shot learning.
• The paper evaluates diverse reasoning strategies, highlighting challenges in applying these methods to complex tasks like Kalamang translation.

Exploring Reasoning Types in LLMs through Task Performance

Introduction to Reasoning in LMs

Recent advances in LLM (LM) research have unveiled a wide spectrum of capabilities, enabling these models to tackle tasks beyond mere text generation. Notably, the ability to perform new tasks via instruction following, few-shot prompting, and instruction inference represents a diverse array of reasoning mechanisms potentially engaged by LMs, including deductive, inductive, and abductive reasoning, respectively. However, the connections between these reasoning types and their effectiveness across different tasks remain underexplored. This gap in understanding forms the basis of our investigation, focusing on comparing the performance of LMs across tasks employing these varied reasoning strategies.

Different Forms of Reasoning in LMs

To comprehensively evaluate the interplay between different reasoning mechanisms and task performance in LMs, we delineate three primary reasoning forms:

• Deductive reasoning, akin to instruction following, where the model applies general rules to specific instances.
• Inductive reasoning, observed in few-shot prompting scenarios, where models generalize rules from specific examples.
• Abductive reasoning, manifested in instruction inference, where models generate hypotheses about task rules from examples provided.

The exploration of these reasoning types aims to reveal how they individually and collectively influence LM capabilities in executing various tasks, spanning from arithmetic functions and artificial language translation to low-resource natural language translation, specifically examining machine translation problems involving the Kalamang language.

Methodological Approach

Our methodological framework encompasses the comparative evaluation of four LMs across three distinct domains: arithmetic function learning, an artificial language learning task, and translation involving Kalamang, a low-resource language. This approach leverages both the generation of hypotheses (instruction inference) and their direct application through instruction following, providing a multifaceted view of reasoning capacities in LMs.

Results and Observations

Instruction Inference and Task Performance

Instruction inference demonstrates notable utility in simpler, synthetic tasks, immensely boosting performance for models under certain conditions. In arithmetic function learning and artificial language translation scenarios, models registering baseline success saw improvements when leveraging self-generated instructions. However, the benefits of instruction inference were not uniformly observed across all tasks, particularly in the complex domain of Kalamang translation, where models faced challenges in generating and applying accurate hypotheses.

Relationship Between Reasoning Types and Learning

An intriguing finding is the apparent dissociation between a model's ability to generate accurate hypotheses (abductive reasoning) and to learn from in-context examples (inductive reasoning). This discrepancy suggests differing underlying mechanisms or model capacities that facilitate these reasoning processes. Models' ability to reason inductively, deducing general rules from examples, appears to operate somewhat independently from their capacity for generating explanatory hypotheses about task-specific rules.

Implications and Future Directions

The insights from this paper underscore the nuanced and variable nature of reasoning across different task domains in LMs. While deductive and inductive reasoning mechanisms showcase robustness in specific task settings, abductive reasoning emerges as a pivotal, yet underexplored, area for enhancing LM capabilities in more complex problem-solving contexts. Future research avenues may include refining instruction inference methods, exploring hybrid reasoning strategies, and developing targeted interventions to bolster abductive reasoning within LMs.

Concluding Remarks

This exploration of reasoning types in LMs through the lens of task performance reveals critical insights into the strengths and limitations of current models. The varying effectiveness of deductive, inductive, and abductive reasoning across different domains highlights the need for continued investigation into how LMs reason and learn. As the field advances, understanding and improving these reasoning capabilities will be vital in unlocking the full problem-solving potential of LLMs.