WILT: A Multi-Turn, Memorization-Robust Inductive Logic Benchmark for LLMs (2410.10998v1)

Abstract: While LLMs have shown impressive capabilities across a wide range of domains, they still encounter significant challenges in reasoning tasks that require gathering evidence over multiple turns and drawing logical conclusions. These challenges present significant obstacles for LLM chat user interfaces, which rely on multi-turn interactions to facilitate effective collaboration. This limitation leads to real-world issues; for example, service chatbots must gather necessary information from customers over multiple turns to diagnose and resolve problems effectively. Despite the multi-turn nature of many real-world LLM use cases, most existing benchmarks rely on carefully curated single-turn tests, which often blur the line between memorization and genuine reasoning. To address this, we introduce the Wason Inductive Logic Test (WILT), a simple yet challenging multi-turn reasoning benchmark designed to resist memorization. WILT is inspired by the Wason 2-4-6 task, where participants must infer a boolean function involving three variables (e.g., $x < y < z$) by proposing test cases (such as $(2, 4, 6)$). In WILT, each test starts from a clean slate, with only the initial instructions provided, preventing models from relying on pre-learned responses. Over several turns, models must interact with the environment by suggesting test cases to narrow the possible hypotheses and ultimately infer the hidden function based on the outcomes. Our findings reveal that LLMs struggle with this task, exhibiting distinct strengths and weaknesses: some are better at narrowing down the hypothesis space by proposing valuable test cases, while others are more adept at deducing the hidden function from observed cases. Despite these variations, the best-performing model achieves only 28% accuracy, highlighting a significant gap in LLM performance on complex multi-turn reasoning tasks.

• The paper introduces a benchmark that challenges LLMs to deduce hidden boolean functions through multi-turn reasoning.
• It employs iterative test case generation and feedback to effectively reduce the hypothesis space in logic tasks.
• Empirical findings reveal that even the best LLMs achieve only 28% accuracy, underscoring gaps in extended reasoning capabilities.

An Analysis of WILT: A Multi-Turn Inductive Logic Benchmark for LLMs

The paper "WILT: A Multi-turn, Memorization-Robust Inductive Logic Benchmark for LLMs" presents the Wason Inductive Logic Test (WILT), a benchmark specifically designed to assess the multi-turn reasoning capabilities of LLMs. The benchmark is a direct response to the limitations observed in existing reasoning tasks, which predominantly rely on single-turn interactions. This emphasis on single-turn tasks often blurs the distinction between memorization and genuine reasoning capabilities. WILT aims to address these limitations by requiring models to engage in multi-turn interactions, gathering evidence over several iterations before deriving logical conclusions.

Core Contributions

The authors introduce WILT as a sophisticated framework to evaluate the inductive logic capabilities of LLMs. The task is inspired by the Wason 2-4-6 task in cognitive psychology, where participants are tasked with discovering a hidden rule through hypothesis testing. In WILT, models propose test cases over multiple turns to infer a hidden boolean function. Each turn provides feedback, and the process continues until the models reach a conclusion or exhaust their attempts. This design implicitly evaluates two key dimensions: the model's ability to effectively reduce the hypothesis space through test case generation and its capacity to synthesize evidence into a succinct and accurate rule.

Empirical Findings

The paper reveals significant challenges faced by existing LLMs in multi-turn reasoning tasks. Most notably, the best-performing model achieves merely 28% accuracy, clearly indicating the complexity and difficulty inherent in WILT tasks. The authors provide detailed insights into the strengths and weaknesses of various models, showcasing how some are adept at generating informative test cases while others excel in deducing hidden functions from accumulated evidence. The variance in model performance underscores the intricate nature of multi-turn reasoning and highlights the gaps in current LLM capabilities.

Implications and Future Directions

The findings from this research have noteworthy implications both practically and theoretically. For practitioners, the results suggest that despite achieving strong performance in single-turn benchmarks, LLMs might not generalize well to tasks necessitating extended reasoning across multiple interactions. This limitation is critical for real-world applications relying on multi-turn interactions, such as chatbots in customer service scenarios, where effective information gathering and problem-solving are crucial.

Theoretically, this paper challenges the existing paradigms of LLM evaluation by stressing the importance of multi-turn reasoning. It opens avenues for future research aimed at enhancing the multi-turn reasoning capabilities of LLMs. Specifically, it suggests the need for new architectures or training methodologies that can better handle the complexities associated with these tasks. Moreover, the benchmark sets a precedent for evaluating models in more realistic, use-case-oriented scenarios, thus pushing the boundary of what LLMs can achieve in practical applications.

Conclusion

The introduction of WILT as a benchmarking tool represents a substantial step forward in evaluating LLMs' reasoning abilities. By focusing on multi-turn interactions and minimizing the risk of memorization, the benchmark provides a more realistic assessment of a model's logical and inductive reasoning capabilities. The insights gathered from this paper call for continued innovation in developing models that can effectively manage the complexities of real-world problem-solving, ensuring their performance is robust across a myriad of applications. The paper sets a foundation for future explorations in improving LLM design and training to meet the nuanced demands of multi-turn reasoning challenges.