LINKED : Eliciting, Filtering and Integrating Knowledge in Large Language Model for Commonsense Reasoning (2024)

Abstract: Large language models (LLMs) sometimes demonstrate poor performance on knowledge-intensive tasks, commonsense reasoning is one of them. Researchers typically address these issues by retrieving related knowledge from knowledge graphs or employing self-enhancement methods to elicit knowledge in LLMs. However, noisy knowledge and invalid reasoning issues hamper their ability to answer questions accurately. To this end, we propose a novel method named eliciting, filtering and integrating knowledge in large language model (LINKED). In it, we design a reward model to filter out the noisy knowledge and take the marginal consistent reasoning module to reduce invalid reasoning. With our comprehensive experiments on two complex commonsense reasoning benchmarks, our method outperforms SOTA baselines (up to 9.0% improvement of accuracy). Besides, to measure the positive and negative impact of the injected knowledge, we propose a new metric called effectiveness-preservation score for the knowledge enhancement works. Finally, through extensive experiments, we conduct an in-depth analysis and find many meaningful conclusions about LLMs in commonsense reasoning tasks.

## introduction commonsense reasoning is one of the key abilities for models to reach artificial general intelligence (agi). to measure it, researchers designed commonsense reasoning tasks @xcite @xcite , which require models to answer questions based on commonsense knowledge (see @xcite 1 for examples). in recent works, large language models (llms) (e.g. palm2 @xcite , @xcite , llama2 @xcite ) have improved performances in this task compared to small models. nevertheless, there is still a considerable gap between them and humans. for instance, on winogrande @xcite question: sarah was a much better surgeon than maria so _ always got the easier cases. ## methodology figure 2 demonstrates the main architecture of our linked method, which is divided into two phases. in the training phase, we aim to train a reward model to address the issue of noisy knowledge. to this end, we first prepare the training data and define the confidence level of the knowledge to distinguish knowledge of different quality ( § 3.1). then, we train the reward model using a ranking task based on the annotated data ( § 3.2). as for mitigating the invalid reasoning issue, we propose the marginal consistent reasoning module in the inference phase. we prompt llms to conduct multiple reasoning processes on one effective rationale and choose the final answer based on the marginal majority vote ( § 3.3). ## conclusion in this paper, we propose a novel method named linked to enhance the llm's performance on commonsense reasoning tasks. specifically, we train a reward model to filter out noisy knowledge in llm's generation and take the marginal consistent reasoning module to reduce invalid reasoning. besides, we design a new metric named eps to evaluate both the effectiveness and harmfulness of different knowledge enhancement methods, which the former metric can not. we conduct comprehensive experiments on four representative commonsense reasoning benchmarks, and experimental results demonstrate that our method significantly outperforms previous baselines.