Zero-Shot Stance Detection in the Wild: Dynamic Target Generation and Multi-Target Adaptation

• URL: http://arxiv.org/abs/2601.19802v3
• Date: Sat, 31 Jan 2026 17:51:50 GMT
• Title: Zero-Shot Stance Detection in the Wild: Dynamic Target Generation and Multi-Target Adaptation
• Abstract summary: We propose a novel task: zero-shot stance detection in the wild with Dynamic Target Generation and Multi-Target Adaptation.<n>We construct a Chinese social media stance detection dataset and design multi-dimensional evaluation metrics.
• Score: 13.424443287821118
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current stance detection research typically relies on predicting stance based on given targets and text. However, in real-world social media scenarios, targets are neither predefined nor static but rather complex and dynamic. To address this challenge, we propose a novel task: zero-shot stance detection in the wild with Dynamic Target Generation and Multi-Target Adaptation (DGTA), which aims to automatically identify multiple target-stance pairs from text without prior target knowledge. We construct a Chinese social media stance detection dataset and design multi-dimensional evaluation metrics. We explore both integrated and two-stage fine-tuning strategies for large language models (LLMs) and evaluate various baseline models. Experimental results demonstrate that fine-tuned LLMs achieve superior performance on this task: the two-stage fine-tuned Qwen2.5-7B attains the highest comprehensive target recognition score of 66.99%, while the integrated fine-tuned DeepSeek-R1-Distill-Qwen-7B achieves a stance detection F1 score of 79.26%.

Related papers

• From Two Passes to One: Compact and Efficient Target-Stance Extraction [0.0]
  We present a one-pass, joint architecture that predicts both in a single forward pass.<n>Standard target-scrubbing practices artificially suppress target prediction accuracy.<n> Retaining explicit target mentions improves F1 by at least 6 points across both target classification and target generation settings.
  arXiv  Detail & Related papers  (2026-09-05T14:07:43Z)
• DetectAnyLLM: Towards Generalizable and Robust Detection of Machine-Generated Text Across Domains and Models [60.713908578319256]
  We propose Direct Discrepancy Learning (DDL) to optimize the detector with task-oriented knowledge.<n>Built upon this, we introduce DetectAnyLLM, a unified detection framework that achieves state-of-the-art MGTD performance.<n>MIRAGE samples human-written texts from 10 corpora across 5 text-domains, which are then re-generated or revised using 17 cutting-edge LLMs.
  arXiv  Detail & Related papers  (2025-09-15T10:59:57Z)
• Zero-Shot Conversational Stance Detection: Dataset and Approaches [24.892337124161983]
  Stance detection aims to identify public opinion towards specific targets using social media data.<n>We manually curate a large-scale, high-quality zero-shot conversational stance detection dataset, named ZS-CSD.<n>We propose SITPCL, a speaker interaction and target-aware prototypical contrastive learning model, and establish the benchmark performance in the zero-shot setting.
  arXiv  Detail & Related papers  (2025-06-21T12:02:06Z)
• Oriented Tiny Object Detection: A Dataset, Benchmark, and Dynamic Unbiased Learning [51.170479006249195]
  We introduce a new dataset, benchmark, and a dynamic coarse-to-fine learning scheme in this study.<n>Our proposed dataset, AI-TOD-R, features the smallest object sizes among all oriented object detection datasets.<n>We present a benchmark spanning a broad range of detection paradigms, including both fully-supervised and label-efficient approaches.
  arXiv  Detail & Related papers  (2024-12-16T09:14:32Z)
• Target-Oriented Object Grasping via Multimodal Human Guidance [6.031799490542892]
  Traditional grasp detection methods analyze the entire scene to predict grasps, leading to redundancy and inefficiency. In this work, we reconsider 6-DoF grasp detection from a target-referenced perspective and propose a Target-Oriented Grasp Network (TOGNet) TOGNet specifically targets local, object-agnostic region patches to predict grasps more efficiently.
  arXiv  Detail & Related papers  (2024-08-20T18:42:56Z)
• SOOD++: Leveraging Unlabeled Data to Boost Oriented Object Detection [68.18620488664187]
  We propose a simple yet effective Semi-supervised Oriented Object Detection method termed SOOD++.<n> Specifically, we observe that objects from aerial images usually have arbitrary orientations, small scales, and dense distribution.<n>Extensive experiments conducted on various oriented object under various labeled settings demonstrate the effectiveness of our method.
  arXiv  Detail & Related papers  (2024-07-01T07:03:51Z)
• Reinforcement Learning for Agile Active Target Sensing with a UAV [10.070339628481445]
  This paper develops a deep reinforcement learning approach to plan informative trajectories. It exploits its current belief of the target states and incorporates inaccurate sensor models for high-fidelity classification. A unique characteristic of our approach is that it is robust to varying amounts of deviations from the true target distribution.
  arXiv  Detail & Related papers  (2022-12-16T01:01:17Z)
• Zero-shot stance detection based on cross-domain feature enhancement by contrastive learning [9.719309795292773]
  We propose a stance detection approach that can efficiently adapt to unseen targets. We first augment the data by masking the topic words of sentences. We then feed the augmented data to an unsupervised contrastive learning module to capture transferable features.
  arXiv  Detail & Related papers  (2022-10-07T07:45:40Z)
• AcroFOD: An Adaptive Method for Cross-domain Few-shot Object Detection [59.10314662986463]
  Cross-domain few-shot object detection aims to adapt object detectors in the target domain with a few annotated target data. The proposed method achieves state-of-the-art performance on multiple benchmarks.
  arXiv  Detail & Related papers  (2022-09-22T10:23:40Z)
• End-to-End Zero-Shot HOI Detection via Vision and Language Knowledge Distillation [86.41437210485932]
  We aim at advancing zero-shot HOI detection to detect both seen and unseen HOIs simultaneously. We propose a novel end-to-end zero-shot HOI Detection framework via vision-language knowledge distillation. Our method outperforms the previous SOTA by 8.92% on unseen mAP and 10.18% on overall mAP.
  arXiv  Detail & Related papers  (2022-04-01T07:27:19Z)
• Learning Target Candidate Association to Keep Track of What Not to Track [100.80610986625693]
  We propose to keep track of distractor objects in order to continue tracking the target. To tackle the problem of lacking ground-truth correspondences between distractor objects in visual tracking, we propose a training strategy that combines partial annotations with self-supervision. Our tracker sets a new state-of-the-art on six benchmarks, achieving an AUC score of 67.2% on LaSOT and a +6.1% absolute gain on the OxUvA long-term dataset.
  arXiv  Detail & Related papers  (2021-03-30T17:58:02Z)
• Dynamic Refinement Network for Oriented and Densely Packed Object Detection [75.29088991850958]
  We present a dynamic refinement network that consists of two novel components, i.e., a feature selection module (FSM) and a dynamic refinement head (DRH) Our FSM enables neurons to adjust receptive fields in accordance with the shapes and orientations of target objects, whereas the DRH empowers our model to refine the prediction dynamically in an object-aware manner. We perform quantitative evaluations on several publicly available benchmarks including DOTA, HRSC2016, SKU110K, and our own SKU110K-R dataset.
  arXiv  Detail & Related papers  (2020-05-20T11:35:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.