Recent work shows large language models can be prompted to generate useful rationales for commonsense question answering (CQA), which can improve the performance of both themselves and other models. However, the cost of deployment and further tuning is relatively expensive for the large models. Some work explores to distill the the rationale-generation ability to convenient small-sized models, yet it typically requires human-authored QA instances during the distillation. In this paper, we propose a novel framework that leverages both knowledge graphs and large language models to synthesize rationale-augmented CQA data. Based on it, we train Leros, a model that can generate helpful rationales to assist generic QA models to accomplish unseen CQA tasks. Empirical results demonstrate Leros can substantially enhance the performance of QA models on five unseen CQA benchmarks, providing better gains than both same-sized counterpart models trained with downstream data and 10x larger language models. Our work reveals a novel way to integrate knowledge from both knowledge graphs and large language models into smaller models. The codes and synthesized resources are publicly available at https://github.com/wchrepo/leros.

Retrieval-augmented language models (RALMs) have demonstrated significant potential in refining and expanding their internal memory by retrieving evidence from external sources. However, RALMs will inevitably encounter knowledge conflicts when integrating their internal memory with external sources. Knowledge conflicts can ensnare RALMs in a tug-of-war between knowledge, limiting their practical applicability. In this paper, we focus on exploring and resolving knowledge conflicts in RALMs. First, we present an evaluation framework for assessing knowledge conflicts across various dimensions. Then, we investigate the behavior and preference of RALMs from the following two perspectives: (1) Conflicts between internal memory and external sources: We find that stronger RALMs emerge with the Dunning-Kruger effect, persistently favoring their faulty internal memory even when correct evidence is provided. Besides, RALMs exhibit an availability bias towards common knowledge; (2) Conflicts between truthful, irrelevant and misleading evidence: We reveal that RALMs follow the principle of majority rule, leaning towards placing trust in evidence that appears more frequently. Moreover, we find that RALMs exhibit confirmation bias, and are more willing to choose evidence that is consistent with their internal memory. To solve the challenge of knowledge conflicts, we propose a method called Conflict-Disentangle Contrastive Decoding (CD2) to better calibrate the model’s confidence. Experimental results demonstrate that our CD2 can effectively resolve knowledge conflicts in RALMs.