Stance detection is a pivotal task in Natural Language Processing (NLP), identifying textual attitudes toward various targets. Despite advances in using Large Language Models (LLMs), challenges persist due to hallucination-models generating plausible yet inaccurate content. Addressing these challenges, we introduce MPVStance, a framework that incorporates Multi-Perspective Verification (MPV) with Retrieval-Augmented Generation (RAG) across a structured five-step verification process. Our method enhances stance detection by rigorously validating each response from factual accuracy, logical consistency, contextual relevance, and other perspectives. Extensive testing on the SemEval-2016 and VAST datasets, including scenarios that challenge existing methods and comprehensive ablation studies, demonstrates that MPVStance significantly outperforms current models. It effectively mitigates hallucination issues and sets new benchmarks for reliability and accuracy in stance detection, particularly in zero-shot, few-shot, and challenging scenarios.

Multi-modal Chain-of-Thought (MCoT) requires models to leverage knowledge from both textual and visual modalities for step-by-step reasoning, which gains increasing attention. Nevertheless, the current MCoT benchmark still faces some challenges: (1) absence of visual modal reasoning, (2) single-step visual modal reasoning, and (3) domain missing, thereby hindering the development of MCoT. Motivated by this, we introduce a novel benchmark (M³CoT) to address the above challenges, advancing the multi-domain, multi-step, and multi-modal CoT. Additionally, we conduct a thorough evaluation involving abundant MCoT approaches on Vision Large Language Models (VLLMs). In addition, we highlight that the current VLLMs still struggle to correctly reason in M³CoT and there is a large gap between VLLMs and human performance in M³CoT, despite their superior results on previous MCoT benchmarks. To our knowledge, we take the first meaningful step toward the multi-domain, multi-step, and multi-modal scenario in MCoT. We hope that M³CoT will serve as a valuable resource, providing a pioneering foundation in multi-domain, multi-step, multi-modal chain-of-thought research.

“针对藏语端到端语音识别研究中存在的建模单元不统一和识别效果不理想的问题,本文提出了一种BPE-Conformer-CTC/Attention端到端藏语语音识别方法。首先,该方法采用了字节对编码算法进行语音建模,通过反复合并出现频率最高的字符对,将文本分割成易于管理、有意义的单元,平衡建模单元的粒度,从而解决藏语语音识别中建模单元不统一的问题。其 次 , 使 用 了Conformer编码器 , 有效地融合了音频序列的全局和局部依赖关系,从而增强了模型的表征能力。最后,通过CTC/Attention联合解码策略,加速了对齐和解码过程,进而提高了识别效果的准确性和效率。在开源数据集XBMU-AMDO31和TIBMD@MUCI上的实验结果表明,所提出的BPE-Conformer-CTC/Attention模型分别取得了9.0%和4.6%的词错误率,相较于基线模型Transformer-CTC/Attention,词错误率分别相对降低了14.2%和30.3%。该研究方法为藏语端到端语音识别任务提供了一种有效的解决方案。”

Stance detection aims at inferring an author’s attitude towards a specific target in a text. Prior methods mainly consider target-related background information for a better understanding of targets while neglecting the accompanying input texts. In this study, we propose to prompt Large Language Models (LLMs) to explicitly extract the relationship between paired text and target as contextual knowledge. We then inject such LLM-driven knowledge into a generation model BART to exploit the rich contexts and semantics. Moreover, to further enhance the decoding capability of BART, a novel prototypical contrastive scheme is designed to align input contents with stance labels. Our experimental results demonstrate the state-of-the-art performance across several publicly available datasets, showcasing effectiveness in both zero-shot and cross-target stance detection scenarios. We publicly release our code to facilitate future research.