Hallucination detection has attracted considerable interest due to the tendency of language models to generate texts that contain hallucinations. Most existing methods start with specific local details directly extracted from text, then aggregate to form the final conclusion. However, this direct extraction approach ignores the global context, leading to isolated details, and is prone to missed or over-detections. In this paper, we present a global-to-local approach for hallucination detection (G2LDetect), which considers the global information of the text before identifying local details. We first construct a global representation of the text by transforming it into a hierarchical tree structure. Afterward, we obtain specific local details from the global tree representation using path-wise identification and perform detection on them. This global-to-local detection process ensures that local details are context-aware and complete, thus making more accurate and reliable detection results. Experimental results show that our global-to-local method outperforms existing methods, especially for longer texts.

Scene understanding in adverse conditions, such as fog, snow, and night, is challenging due to the visual appearance degeneration. In this context, we propose a Cross-modal Semantic Compensation Adaptation method (CroPe) for scene understanding. Distinct from the existing methods, which only use the visual information to learn the domain-invariant features, CroPe establishes a visual-textual paradigm which provides textual semantic compensation for visual features, enabling the model to learn more consistent representations. We propose the Complementary Perceptual Text Generation (CPTG) module which generates a set of multi-level complementary-perceptive text embeddings incorporating both generalization and domain awareness. To achieve cross-modal semantic compensation, the Reverse Chain Text-Visual Fusion (RCTVF) module is developed. By the unified attention and reverse decoding chain, compensation information is successively fused to the visual features from the deep (semantic dense) to shallow (semantic sparse) features, maximizing compensation gain. CroPe yields competitive results under all adverse conditions and significantly improves the state-of-the-art performance by 6.5 mIoU for ACDC-Night dataset and 1.2 mIoU for ACDC-All dataset, respectively.

Zeqi Tan, Yongliang Shen, Xiaoxia Cheng, Chang Zong, Wenqi Zhang, Jian Shao, Weiming Lu, Yueting Zhuang. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Extractive summarization is a process that extracts salient sentences from the original document to create a summary. Typically, the process relies on sentence labels, which indicate whether a sentence should be included in the summary or not. Widely used sentence labels are constructed by greedily selecting sentences one by one from the original document. However, this greedy manner obtains sub-optimal sentence labels and can’t adequately distinguish the importance of individual sentences. Exploring new labeling schemes to construct superior and informative sentence labels is necessary. In this work, we take a cooperative game perspective on sentence label construction. We treat sentences as different players and label construction as a cooperative game between them. In such a perspective, we design an optimal and soft label, termed the Shapley Value of sentences (SVS) to reasonably discern the importance of individual sentences. To alleviate the computational complexity of SVS, we adopt a Monte Carlo sampling-based method to approximate it. Experimental results demonstrate that our proposed SVS label performs better on existing benchmarks across sentence-level and summary-level, in supervised and zero-shot settings.

Improving the reasoning capabilities of large language models (LLMs) has attracted considerable interest. Recent approaches primarily focus on improving the reasoning process to yield a more precise final answer. However, in scenarios involving contextually aware reasoning, these methods neglect the importance of first identifying logical relationships from the context before proceeding with the reasoning. This oversight could lead to a superficial understanding and interaction with the context, potentially undermining the quality and reliability of the reasoning outcomes. In this paper, we propose an information re-organization (\textbf{InfoRE}) method before proceeding with the reasoning to enhance the reasoning ability of LLMs. Our re-organization method involves initially extracting logical relationships from the contextual content, such as documents or paragraphs, and subsequently pruning redundant content to minimize noise. Then, we utilize the re-organized information in the reasoning process. This enables LLMs to deeply understand the contextual content by clearly perceiving these logical relationships, while also ensuring high-quality responses by eliminating potential noise. To demonstrate the effectiveness of our approach in improving the reasoning ability, we conduct experiments using Llama2-70B, GPT-3.5, and GPT-4 on various contextually aware multi-hop reasoning tasks. Using only a zero-shot setting, our method achieves an average absolute improvement of 4\% across all tasks, highlighting its potential to improve the reasoning performance of LLMs.

Joint entity and relation extraction has been a core task in the field of information extraction.Recent approaches usually consider the extraction of relational triples from a stereoscopic perspective, either learning a relation-specific tagger or separate classifiers for each relation type.However, they still suffer from error propagation, relation redundancy and lack of highlevel connections between triples.To address these issues, we propose a novel query-based approach to construct instance-level representations for relational triples.By metric-based comparison between query embeddings and token embeddings, we can extract all types of triples in one step, thus eliminating the error propagation problem.In addition, we learn the instance-level representation of relational triples via contrastive learning.In this way, relational triples can not only enclose rich classlevel semantics but also access to high-order global connections.Experimental results show that our proposed method achieves the state of the art on five widely used benchmarks.

Complex knowledge base question answering can be achieved by converting questions into sequences of predefined actions.However, there is a significant semantic and structural gap between natural language and action sequences, which makes this conversion difficult.In this paper, we introduce an alignment-enhanced complex question answering framework, called ALCQA, which mitigates this gap through question-to-action alignment and question-toquestion alignment.We train a question rewriting model to align the question and each action, and utilize a pretrained language model to implicitly align the question and KG artifacts.Moreover, considering that similar questions correspond to similar action sequences, we retrieve top-k similar question-answer pairs at the inference stage through question-to-question alignment and propose a novel reward-guided action sequence selection strategy to select from candidate action sequences.We conduct experiments on CQA and WQSP datasets, and the results show that our approach outperforms state-of-the-art methods and obtains a 9.88% improvements in the F1 metric on CQA dataset.Our source code is available at https://github.com/TTTTTTTTy/ALCQA.