Multi-Hop Question Answering (MHQA) is crucial for evaluating the model's capability to integrate information from diverse sources.However, creating extensive and high-quality MHQA datasets is challenging: (i) manual annotation is expensive, and (ii) current synthesis methods often produce simplistic questions or require extensive manual guidance.This paper introduces HopWeaver, the first cross-document framework synthesizing authentic multi-hop questions without human intervention.HopWeaver synthesizes bridge and comparison questions through an innovative pipeline that identifies complementary documents and constructs authentic reasoning paths to ensure true multi-hop reasoning.We further present a comprehensive system for evaluating the synthesized multi-hop questions.Empirical evaluations demonstrate that the synthesized questions achieve comparable or superior quality to human-annotated datasets at a lower cost.Our framework provides a valuable tool for the research community: it can automatically generate challenging benchmarks from any raw corpus, which opens new avenues for both evaluation and targeted training to improve the reasoning capabilities of advanced question answering models, especially in domains with scarce resources.The code for HopWeaver is publicly available 1 .

Disagreement detection is a crucial task in natural language processing (NLP), particularly in analyzing online discussions and social media content.Large language models (LLMs) have demonstrated significant advancements across various NLP tasks.However, the performance of LLMs in disagreement detection is limited by two issues: conceptual gap and reasoning gap.In this paper, we propose a novel two-stage framework, Concept Alignment and Reasoning Enhancement (CARE), to tackle the issues.The first stage, Concept Alignment, addresses the gap between expert and model by performing sub-concept taxonomy extraction, aligning the model's comprehension with human experts.The second stage, Reasoning Enhancement, improves the model's reasoning capabilities by introducing curriculum learning workflow, which includes rationale to critique and counterfactual to detection for reducing spurious association.Extensive experiments on disagreement detection task demonstrate the effectiveness of our framework, showing superior performance in zero-shot and supervised learning settings, both within and across domains.

Topic modeling is a powerful unsupervised tool for knowledge discovery.However, existing work struggles with generating limited-quality topics that are uninformative and incoherent, which hinders interpretable insights from managing textual data.In this paper, we improve the original variational autoencoder framework by incorporating contextual and graph information to address the above issues.First, the encoder utilizes topic fusion techniques to combine contextual and bag-of-words information well, and meanwhile exploits the constraints of topic alignment and topic sharpening to generate informative topics.Second, we develop a simple word co-occurrence graph information fusion strategy that efficiently increases topic coherence.On three benchmark datasets, our new framework generates more coherent and diverse topics compared to various baselines, and achieves strong performance on both automatic and manual evaluations.We make our code available for reproduction 1 .