Papers by Yixiao Wang
VALU: A Benchmark for Video Anomaly Temporal Localization and Understanding at Multiple Semantic Levels (2026.acl-long)
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Yixiao He, Menghao Zhang, Haifeng Sun, Jing Wang, Kangheng Lin, Jinghan Wang, Chenye Xu, Pengfei Ren, Qi Qi, Jingyu Wang
| Challenge: | Recent advances in Video Large Language Models (Video-LLMs) enhance the ability of VAU models to describe and interpret anomalies. |
| Approach: | They propose a benchmark that explicitly defines anomalies across five semantic levels and provides detailed temporal boundaries and detailed textual descriptions for each. |
| Outcome: | The proposed benchmark defines anomalies across five semantic levels and provides detailed descriptions for each. |
Personal Large Language Model Agents: A Case Study on Tailored Travel Planning (2024.emnlp-industry)
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Harmanpreet Singh, Nikhil Verma, Yixiao Wang, Manasa Bharadwaj, Homa Fashandi, Kevin Ferreira, Chul Lee
| Challenge: | Large Language Models (LLMs) are becoming more autonomous and capable of handling real-world tasks through their access to tools, various planning strategies, and memory, referred to as LLM agents. |
| Approach: | They introduce a personalized version of TravelPlanner and establish baselines for personal LLM agents by comparing generic and personal plans. |
| Outcome: | The proposed model encapsulates user-related information, preferences, and personal concepts and provides baselines for personal LLM agents. |
How Do LLMs "Trust" Unknown Knowledge? An Unknown Knowledge Based Jailbreak Attack (2026.findings-acl)
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| Challenge: | Existing research on how to effectively utilize unknown knowledge has focused on how it can be used to enhance LLMs' performance in specialized fields. |
| Approach: | They propose a completely unrestricted and fully randomized jailbreak attack that embeds malicious queries within trust-enhanced unknown knowledge. |
| Outcome: | The proposed method achieves 99% to 100% ASR on all tested LLMs, including the latest GPT-5.1, and becomes SOTA. |
BotChat: Evaluating LLMs’ Capabilities of Having Multi-Turn Dialogues (2024.findings-naacl)
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Haodong Duan, Jueqi Wei, Chonghua Wang, Hongwei Liu, Yixiao Fang, Songyang Zhang, Dahua Lin, Kai Chen
| Challenge: | Modern Large Language Models (LLMs) facilitate high-quality, multi-turn dialogues with humans, but human-based evaluation of such a capability requires substantial manual effort. |
| Approach: | They propose to evaluate LLMs' ability to emulate human-like, multi-turn conversations using an LLM-centric approach. |
| Outcome: | The proposed model emulates human-like, multi-turn conversations using an LLM-centric approach. |
Sentence Selection Strategies for Distilling Word Embeddings from BERT (2022.lrec-1)
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| Challenge: | Using language models to learn word embeddings is a key feature of transformer-based language models. |
| Approach: | They propose to use language models to learn high-quality word vectors from as few as 5 to 10 sentences with a careful selection strategy. |
| Outcome: | The proposed strategies can learn high-quality word vectors from as few as 5 to 10 sentences. |
Plot2Code: A Comprehensive Benchmark for Evaluating Multi-modal Large Language Models in Code Generation from Scientific Plots (2025.findings-naacl)
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| Challenge: | Multi-modal Large Language Models have shown remarkable progress in visual contexts, yet their ability to convert visual figures into executable code remains underexplored. |
| Approach: | They propose to use a set of visual coding metrics to assess MLLMs' visual . pass rate, text-match ratio, and GPT-4V rating judgement to assess the quality of generated code and rendered images. |
| Outcome: | The proposed benchmark includes 132 high-quality matplotlib plots across six plot types, as well as 150 and 86 plots from Python’s and R’s plotly libraries respectively, totaling 368 plots. |
Chinese Toxic Language Mitigation via Sentiment Polarity Consistent Rewrites (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) can be effective at rewriting toxic content, but they often default to overly polite rewrites, distorting the emotional tone and communicative intent. |
| Approach: | They evaluate 17 large language models with variant architectures to evaluate their ability to rewrite toxic content while preserving the speaker's original intent. |
| Outcome: | The first Chinese detoxification dataset explicitly designed to preserve sentiment polarity is evaluated across five real-world scenarios. |
LLaMA Pro: Progressive LLaMA with Block Expansion (2024.acl-long)
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| Challenge: | Existing studies have demonstrated that pre-trained LLMs are limited in certain domains, such as programming, mathematics, biomedical, or finance. |
| Approach: | They propose a new post-pretraining method with an expansion of Transformer blocks to tune the expanded blocks using only new corpus, efficiently and effectively improving the model’s knowledge while mitigating forgetting. |
| Outcome: | The proposed model outperforms existing models in programming and math and its instruction-following counterpart LLaMA Pro-8.3B in general tasks, programming, and mathematics. |
kNN-LM Does Not Improve Open-ended Text Generation (2023.emnlp-main)
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| Challenge: | Interpolation-based retrieval-augmented language models (LMs) are a subtype of retrieval augmented language model that computes the probability of the next token by interpolating between the softmax distribution of the original LM and a token distribution formed by retrieving over an external datastore. |
| Approach: | They propose to interpolate the predicted distribution of the next word with a distribution formed from the most relevant retrievals for a given prefix. |
| Outcome: | The proposed methods do not exhibit improvements in open-ended generation quality, as measured by automatic evaluation metrics and human evaluations. |
Investigating the Personality Consistency in Quantized Role-Playing Dialogue Agents (2024.emnlp-industry)
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| Challenge: | Using the Big Five personality traits model, we evaluate how stable assigned personalities are for Quantized Role-Playing Dialog Agents (QRPDA) during multi-turn interactions. |
| Approach: | They propose a non-parametric method to evaluate the stability of assigned personalities in quantized large language models (LLMs) for role-playing scenarios. |
| Outcome: | The proposed method shows that it maintains consistent personality traits in QRPDA, and it is more reliable in real-world applications. |
SaCa: A Highly Compatible Reinforcing Framework for Knowledge Graph Embedding via Structural Pattern Contrast (2025.findings-emnlp)
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| Challenge: | Existing knowledge Graph Embedding approaches lack structural semantics of knowledge graphs . structure-aware calibration (SaCa) is a framework designed to calibrate KGEs based on global structural patterns. |
| Approach: | a new framework is designed to calibrate knowledge graphs using global structural patterns. |
| Outcome: | a new framework can calibrate KGE models using global structural patterns . the framework consistently boosts performance across ten models on link prediction and entity classification tasks . |
GRPO-CARE: Consistency-Aware Reinforcement Learning for Multimodal Reasoning (2026.findings-acl)
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| Challenge: | Recent reinforcement learning approaches have advanced reasoning in Large Language Models (LLMs), yet their adaptation to multimodal LLMs remains underexplored. |
| Approach: | They propose a reinforcement learning framework that eliminates KL penalties and rewards consistency . they propose GRPO-CARE, which outperforms standard GR PO, with a base reward for accuracy and an adaptive bonus for consistency. |
| Outcome: | The proposed framework outperforms standard GRPO on the most difficult evaluation level and reasoning consistency test benchmarks. |
Evaluating and Mitigating Object Hallucination in Large Vision-Language Models: Can They Still See Removed Objects? (2025.naacl-long)
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| Challenge: | LVLMs often mistakenly determine objects as present in images where they do not exist . authors propose a new benchmark to evaluate object hallucinations by removing objects from images and asking the model whether it can still see the removed objects. |
| Approach: | They propose a benchmark to evaluate object hallucinations by removing objects from images . they propose oDPO, a direct preference optimization objective based on visual objects . |
| Outcome: | The proposed benchmark reduces the likelihood of object hallucinations by removing objects from images and asking the model whether it can still see the removed objects. |