As large language models (LLMs) are more frequently used in retrieval-augmented generation pipelines, it is increasingly relevant to study their behavior under knowledge conflicts. Thus far, the role of the source of the retrieved information has gone unexamined. We address this gap with a novel framework to investigate how source preferences affect LLM resolution of inter-context knowledge conflicts in English, motivated by interdisciplinary research on credibility. By using synthetic sources, we study preferences for different types of sources without inheriting the biases of specific real-world sources. With a comprehensive, tightly-controlled evaluation of 13 open-weight LLMs, we find that LLMs prefer institutionally-corroborated information (e.g., government or newspaper sources) over information from people and social media. However, these source preferences can be reversed by simply repeating information from less credible sources. To mitigate repetition effects and maintain consistent preferences, we propose a novel method that reduces repetition bias by up to 79.2%, while also maintaining at least 72.5% of original preferences. We release all data and code to encourage future work on credibility and source preferences in knowledge-intensive NLP.

In this work, we investigate cross-lingual methods for metaphor detection of adjective-noun phrases in three languages (English, German and Polish). We explore the potential of minimalistic neural networks supported by static embeddings as a light-weight alternative for large transformer-based language models. We measure performance in zero-shot experiments without access to annotated target language data and aim to find low-resource improvements for them by mainly focusing on a k-shot paradigm. Even by incorporating a small number of phrases from the target language, the gap in accuracy between our small networks and large transformer architectures can be bridged. Lastly, we suggest that the k-shot paradigm can even be applied to models using machine translation of training data.