ConstraintChecker: A Plugin for Large Language Models to Reason on Commonsense Knowledge Bases (2401.14003v1)

Abstract: Reasoning over Commonsense Knowledge Bases (CSKB), i.e. CSKB reasoning, has been explored as a way to acquire new commonsense knowledge based on reference knowledge in the original CSKBs and external prior knowledge. Despite the advancement of LLMs (LLM) and prompt engineering techniques in various reasoning tasks, they still struggle to deal with CSKB reasoning. One of the problems is that it is hard for them to acquire explicit relational constraints in CSKBs from only in-context exemplars, due to a lack of symbolic reasoning capabilities (Bengio et al., 2021). To this end, we proposed ConstraintChecker, a plugin over prompting techniques to provide and check explicit constraints. When considering a new knowledge instance, ConstraintChecker employs a rule-based module to produce a list of constraints, then it uses a zero-shot learning module to check whether this knowledge instance satisfies all constraints. The acquired constraint-checking result is then aggregated with the output of the main prompting technique to produce the final output. Experimental results on CSKB Reasoning benchmarks demonstrate the effectiveness of our method by bringing consistent improvements over all prompting methods. Codes and data are available at \url{https://github.com/HKUST-KnowComp/ConstraintChecker}.

ConstraintChecker: Enhancing CSKB Reasoning in LLMs

The paper "ConstraintChecker: A Plugin for LLMs to Reason on Commonsense Knowledge Bases" explores a significant challenge in the development and implementation of LLMs concerning their reasoning over Commonsense Knowledge Bases (CSKBs). Despite advancements in LLMs and associated prompt engineering techniques, these models are hindered by their limited proficiency in identifying and applying explicit relational constraints inherent to CSKBs. This shortfall arises mainly from their limited symbolic reasoning capabilities, a complex problem linked to broader deficiencies in deep learning approaches with respect to symbolic reasoning.

Overview of ConstraintChecker

To address these challenges, the authors introduce ConstraintChecker, a plugin designed to enhance LLMs' performance in reasoning tasks by integrating a rule-based module that identifies explicit constraints and a zero-shot learning module that assesses whether new instances of knowledge satisfy these constraints. This method involves the following key steps:

1. Rule-Based Module: This component generates a list of constraints relevant to a specifically identified knowledge relation in CSKBs.
2. Zero-Shot Learning Module: Utilizes LLMs to evaluate whether a given knowledge instance fulfills the constraints set by the rule-based module.
3. Aggregation and Output: The ConstraintChecker integrates the constraint-checking results with primary prompt outputs to deliver the final judgment on whether a knowledge instance is commonsensical.

Experimental Evaluation

The ConstraintChecker was evaluated using CSKB Reasoning benchmarks from CKBPv2 and a synthetic dataset derived from ATOMIC$_{20}^{20}$. The trials employed both ChatGPT (gpt-3.5-turbo-0301) and GPT3.5 (text-davinci-003) as the backbone models. The results consistently demonstrated that integrating ConstraintChecker with prompting methods resulted in performance improvements across various metrics, including accuracy and F1 score.

Implications and Future Directions

From a practical standpoint, this paper advances the utility of LLMs in handling more nuanced reasoning tasks by refining their approach to constraint satisfaction, an area where traditional deep learning models have struggled. By demonstrating a plug-and-play architecture, ConstraintChecker offers an adaptable solution that can potentially be applied across different models and tasks, indicating a promising direction for the enhancement of symbolic reasoning capabilities within LLMs.

Theoretically, the research highlights the need for continued exploration into the integration of rule-based logic with neural network models to tackle symbolic reasoning challenges. The separation between learning statistical correlations and understanding rule-based logic is a critical barrier in advanced AI development, suggesting that methodologies like ConstraintChecker could play a pivotal role in bridging this gap.

Conclusion

The introduction of ConstraintChecker marks a valuable step forward in enabling LLMs to better process and adapt to CSKB tasks with heightened accuracy. The improvement over existing methodologies highlights the potential for further research and development in this domain, suggesting that combining symbolic reasoning with deep learning may continue to enhance AI capabilities significantly. Future work could focus on broadening this methodology to other areas of AI reasoning, thereby improving the robustness and applicability of LLMs in complex, real-world tasks.