Improving Contextual Faithfulness of Large Language Models via Retrieval Heads-Induced Optimization (2025 · 11 citations)
The paper addresses faithfulness hallucination in retrieval-augmented long-form question answering (LFQA), where a model writes paragraph-length answers containing claims that the retrieved documents do not support or that contradict them.
Culture-Aware Machine Translation in Large Language Models: Benchmarking and Investigation (2026 · 2 citations)
LLMs translate general text well, but little is known about how they handle culturally loaded content. The authors build CanMT, a parallel benchmark drawn from classic novels and their professional translations. It covers 12 directions among English, Chinese, Japanese, Spanish and Russian.
x1: Learning to Think Adaptively Across Languages and Cultures (2026 · 1 citation)
Most reasoning LLMs think mainly in one dominant language, usually English, even when a question is posed in another language or depends on a specific culture.

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Updated 2 November 2026