The paper introduces TreeFuse, a training‑free framework that builds a semantic hierarchy from a full document and uses it to select and organize evidence for a query. By combining structural–semantic scoring with lexical attribution, TreeFuse retains supporting facts and relations while focusing the context on the question. Experiments on four large‑context models show a 1.72‑point improvement in QA‑F1 over full‑context prompting and a 58.55% reduction in answer‑context length.
By Zhe Xu, Tianhao Liao, Zhenqing Ling, Ying Shen, Yaliang Li, Daoyuan Chen
CARA is a training‑free, causally guided retrieval agent designed for question answering over text‑rich knowledge graphs. It evaluates retrieved evidence using two causal criteria—consequentiality and essentiality—alongside path consistency, and aggregates results from multiple agents via reciprocal rank fusion. Experiments on three STaRK datasets show that CARA outperforms existing retrievers, improving answer quality and faithfulness while examining fewer graph nodes and requiring fewer retrieval operations.
By Weicheng Liu, Xiangcheng Wu, Depeng Xu, Haopeng Zhang, Xi Niu
Knowledge-base question answering (KBQA) systems rely on effective retrieval and reasoning mechanisms to generate accurate answers from external knowledge sources. However, developing reliable KBQA systems for low-resource languages such as Bangla remains challenging due to limited retrieval-focused research, scarce language resources, and difficulties in grounding generated responses in external knowledge.
arXiv:2606. 06197v1 Announce Type: cross Abstract: Question answering (QA) systems have achieved notable progress with the advent of large language models (LLMs).
By Hafez Abdelghaffar, Ahmed Alansary, Ali Hamdi
arXiv:2607. 19398v1 Announce Type: new Abstract: Multi-entity compositional questions pose significant challenges to existing retrieval-augmented language models.
By Junyi Wang
arXiv:2606. 05901v1 Announce Type: cross Abstract: Large language models (LLMs) have fundamentally transformed the landscape of Natural Language Processing.
By Christopher J. Wedge, Joshua Stutter, Danny Dixon, Jacek Ca{\l}a