Papers by Qianlong Wang
Improving In-Context Learning with Prediction Feedback for Sentiment Analysis (2024.findings-acl)
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| Challenge: | Large language models (LLMs) have achieved promising results in sentiment analysis through the in-context learning paradigm. |
| Approach: | They propose a framework that incorporates prior predictions and feedback to improve sentiment understanding by incorporating prior feedback and leveraging a feedback-driven prompt. |
| Outcome: | The proposed framework improves on nine sentiment analysis datasets with an average improvement of 5.95% over conventional methods. |
TwiUSD: A Benchmark Dataset and Structure-Aware LLM Framework for User Stance Detection (2026.acl-long)
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| Challenge: | Existing benchmarks for political user-level stance detection rely on noisy heuristics or distant supervision. |
| Approach: | They propose a large-scale, expert-annotated benchmark for political user-level stance detection with explicit social network structure that integrates user content and followee signals. |
| Outcome: | The proposed framework outperforms baselines in terms of quality and reliability. |
Comprehensive and Efficient Distillation for Lightweight Sentiment Analysis Models (2025.emnlp-main)
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| Challenge: | Recent efforts to develop lightweight and practical sentiment analysis models are limited by manual instruction and large-scale user texts. |
| Approach: | They propose a framework for sentiment analysis that uses attribute-based instruction construction and difficulty-based data filtering to distill knowledge. |
| Outcome: | The proposed framework outperforms baseline methods in data efficiency and performance. |
Reducing Spurious Correlations in Aspect-based Sentiment Analysis with Explanation from Large Language Models (2023.findings-emnlp)
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| Challenge: | Aspect-based sentiment analysis models are susceptible to learning spurious correlations between words . a recent study shows that feature engineering is time-consuming and costly . |
| Approach: | They propose to use a template to prompt LLMs to generate an appropriate explanation for the sentiment polarity of each aspect to reduce spurious correlations. |
| Outcome: | The proposed methods improve ABSA models and their generalization ability. |
Error Comparison Optimization for Large Language Models on Aspect-Based Sentiment Analysis (2025.acl-long)
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| Challenge: | Existing methods for aspect-based sentiment analysis (ABSA) only compare current predictions and labels on each sample, yet fail to perceive and understand its error outputs from different degrees. |
| Approach: | They propose a framework that can perceive and understand the degree of errors by learning from comparative error pairs. |
| Outcome: | The proposed framework exceeds baselines and achieves the desired performance. |
PiKGL: Leveraging Pruned Knowledge Graphs for Explainable Stance Detection (2026.tacl-1)
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Bingbing Wang, Jingjie Lin, Zhixin Bai, Xintong Song, Qianlong Wang, Min Yang, Xi Zeng, Jing Li, Ruifeng Xu
| Challenge: | Experimental results demonstrate that a Pruned interpretable knowledge Graph Learning framework for explainable stance detection is state-of-the-art for social media stance prediction. |
| Approach: | They propose a Pruned interpretable knowledge Graph Learning framework for explainable stance detection that incorporates commonsense knowledge and prunes redundant information to ensure precision and minimize noise. |
| Outcome: | The proposed framework achieves state-of-the-art on three public datasets. |
Label Correction Model for Aspect-based Sentiment Analysis (2020.coling-main)
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| Challenge: | Existing models for aspect-based sentiment analysis ignore a phenomenon: aspect boundary label and sentiment label can correct each other. |
| Approach: | They propose a model that uses aspect boundary label and sentiment label to correct each other . they evaluate the model on three benchmark datasets and evaluate its performance . |
| Outcome: | The proposed model performs state-of-the-art on three benchmark datasets. |
Targeted Distillation for Sentiment Analysis (2025.emnlp-main)
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| Challenge: | Recent studies demonstrate that large language models exhibit remarkable capabilities and achieve state-of-the-art performance in diverse sentiment analysis tasks. |
| Approach: | They propose a distillation framework that decouples knowledge from alignment and introduces a sentiment analysis benchmark that covers a diverse set of tasks. |
| Outcome: | The proposed framework improves models' generalization to unseen tasks and their generalization is strong against existing small-scale models. |
Progressive Self-Training with Discriminator for Aspect Term Extraction (2021.emnlp-main)
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| Challenge: | Existing approaches to extract aspect terms from review sentences are limited due to lack of annotated data. |
| Approach: | They propose to refine conventional self-training to progressive self-teaching to reduce noise . they use a discriminator to filter the noisy pseudo-labels. |
| Outcome: | The proposed model outperforms baseline models and achieves state-of-the-art performance on four SemEval datasets. |
Towards Preference Following in Tool Calling Language Agents (2026.findings-acl)
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| Challenge: | Currently, large language model (LLM)-based agents can't follow user preferences when calling tools. |
| Approach: | They propose a benchmark to evaluate agents' ability to identify personalized user preferences from interaction histories and to adhere to these preferences when calling tools. |
| Outcome: | The proposed model achieves 51.16% accuracy on the APOLLO benchmark, while GPT-4o achieves only 51.13% accuracy. |
In-Context Example Retrieval from Multi-Perspectives for Few-Shot Aspect-Based Sentiment Analysis (2024.lrec-main)
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| Challenge: | Existing approaches to solve few-shot aspect-based sentiment analysis (ABSA) are suboptimal for this task because of in-context examples . |
| Approach: | They propose to retrieve in-context examples for few-shot aspect-based sentiment analysis . they construct positive and negative pairs from three perspectives and train the retriever . |
| Outcome: | The proposed retrieval framework outperforms baselines on four ABSA datasets. |
In-context Learning for Few-shot Multimodal Named Entity Recognition (2023.findings-emnlp)
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| Challenge: | Existing methods for named entity recognition are time-consuming and laborintensive. |
| Approach: | They propose a few-shot multimodal named entity recognition task that uses few examples to locate and identify named entities for a text-image pair. |
| Outcome: | The proposed framework outperforms baselines under several few-shot settings. |
CoreEval: Automatically Building Contamination-Resilient Datasets with Real-World Knowledge toward Reliable LLM Evaluation (2025.acl-long)
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| Challenge: | Publicly available datasets can be used to evaluate performance of large language models . however, contamination of test data can artificially inflate model performance . |
| Approach: | They propose a Contamination-resilient Evaluation strategy that updates data with real-world knowledge. |
| Outcome: | The proposed evaluation strategy can be used to update datasets with real-world knowledge. |
Understanding and Mitigating Spurious Signal Amplification in Test-Time Reinforcement Learning for Math Reasoning (2026.findings-acl)
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| Challenge: | a framework to mitigate spurious optimization signals is proposed for test-time reinforcement learning (TTRL) Reinforcement learning with verifiable rewards (RLVR) is an effective paradigm for improving large language models on structured challenging reasoning tasks. |
| Approach: | They propose a framework to mitigate spurious optimization signals from label noise . they propose to use a frequency-based sampling strategy to exclude ambiguous samples . |
| Outcome: | The proposed framework outperforms existing TTRL baselines on three large language models across multiple mathematical reasoning benchmarks. |
DS2-ABSA: Dual-Stream Data Synthesis with Label Refinement for Few-Shot Aspect-Based Sentiment Analysis (2025.acl-long)
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| Challenge: | Existing methods for annotating data are time-consuming and labor-intensive . Existing low-resource solutions comprise data augmentation and in-context learning . |
| Approach: | They propose a dual-stream data synthesis framework for few-shot ABSA . it leverages key-point-driven and instance-driven LLMs to generate diverse data . |
| Outcome: | Extensive experiments show that DS2-ABSA outperforms existing methods . previous studies have shown that the proposed approach generates diverse data . |