HERO'S JOURNEY: Testing Complex Rule Induction with Text Games

Problem
The paper addresses the gap in evaluating rule induction capabilities of large language models (LLMs) in complex, goal-directed episodic tasks. Existing benchmarks do not adequately test the ability of models to infer and execute hidden rules from demonstrations. This work introduces HERO’S JOURNEY, a novel benchmark designed to systematically assess these capabilities across various tasks. The authors note that this is a preprint and has not yet undergone peer review.

Method
HERO’S JOURNEY comprises eight distinct tasks that fall into two families: attribute induction and procedural induction. Each task is structured around four types of rules, allowing for a comprehensive evaluation of rule induction capabilities. The benchmark incorporates controllable lexical grounding and identifiability conditions to ensure that the rules can be effectively inferred and executed by agents. The authors evaluate state-of-the-art LLMs, focusing on their ability to perform multi-step execution based on the inferred rules. The evaluation methodology includes induction-specific steering methods aimed at enhancing performance on attribute tasks, although these methods do not yield consistent improvements for procedural tasks.

Results
The evaluation reveals that while LLMs demonstrate some capacity for rule induction, their performance is inconsistent across the different tasks. Specifically, the models show limited success in procedural induction, highlighting a significant gap in their capabilities. The authors report that induction-specific steering methods improve performance on attribute tasks, but do not provide reliable gains for procedural tasks. The results indicate that the execution of inferred rules presents a bottleneck for the models, with surface semantics having a negligible impact on overall performance. The paper provides quantitative results, although specific numbers and baseline comparisons are not detailed in the abstract.

Limitations
The authors acknowledge that the performance of LLMs is uneven across tasks, particularly in procedural induction, which remains an open challenge. They also note that the execution bottleneck may hinder the practical application of rule induction in real-world scenarios. An additional limitation not explicitly mentioned by the authors is the potential overfitting of models to the specific tasks in the benchmark, which may not generalize to broader applications of rule induction.

Why it matters
The introduction of HERO’S JOURNEY as a benchmark for rule induction is significant for advancing the understanding of LLM capabilities in complex reasoning tasks. By highlighting the limitations of current models, this work sets the stage for future research aimed at improving procedural induction and execution in AI systems. The findings underscore the need for more robust methodologies to enhance the performance of LLMs in goal-directed tasks, which is crucial for applications in natural language understanding and interactive AI. This research contributes to the ongoing discourse in the field, as published in arXiv.