The semantic parsing-based method is an important research branch for
knowledge-based question answering. It usually generates executable programs
lean upon the question and then conduct them to reason answers over a knowledge
base. Benefit from this inherent mechanism, it has advantages in the
performance and the interpretability. However,traditional semantic parsing
methods usually generate a complete program before executing it, which
struggles with multi-hop question answering over heterogeneous knowledge.
Firstly,a complete multi-hop program relies on multiple heterogeneous
supporting facts, and it is difficult for models to receive these facts
simultaneously. Secondly,these methods ignore the interaction information
between the previous-hop execution result and the current-hop program
generation. To alleviate these challenges, we propose a self-iterative
framework for multi-hop program generation (HopPG) over heterogeneous
knowledge, which leverages the previous-hop execution results to retrieve
supporting facts and generate subsequent programs iteratively. We evaluate our
model on MMQA-T^2. The experimental results show that HopPG outperforms
existing semantic-parsing-based baselines, especially on the multi-hop
questions.

我们提出了一个自我迭代的多跳程序生成框架（HopPG），用于异构知识上的多跳程序生成，它利用前一跳的执行结果来检索支持事实并迭代生成后续程序。我们在 MMQA-T^2 上评估了我们的模型，实验结果表明 HopPG 在多跳问题上优于现有的语义解析基线模型。

HopPG：针对异构知识进行多跳问答的自迭代程序生成

HopPG: Self-Iterative Program Generation for Multi-Hop Question  Answering over Heterogeneous Knowledge

The analysis and mining of user heterogeneous behavior are of paramount
importance in recommendation systems. However, the conventional approach of
incorporating various types of heterogeneous behavior into recommendation
models leads to feature sparsity and knowledge fragmentation issues. To address
this challenge, we propose a novel approach for personalized recommendation via
Large Language Model (LLM), by extracting and fusing heterogeneous knowledge
from user heterogeneous behavior information. In addition, by combining
heterogeneous knowledge and recommendation tasks, instruction tuning is
performed on LLM for personalized recommendations. The experimental results
demonstrate that our method can effectively integrate user heterogeneous
behavior and significantly improve recommendation performance.

通过从用户的异构行为信息中提取和融合异构知识，借助大型语言模型（LLM）实现个性化推荐，并通过结合异构知识与推荐任务，在 LLM 上进行指令调整以显著提高推荐性能。实验结果表明，我们的方法能够有效整合用户的异构行为并显著改善推荐性能。