Papers by Yixuan Tang
FinEntity: Entity-level Sentiment Classification for Financial Texts (2023.emnlp-main)
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| Challenge: | FinEntity annotates financial entity spans and their sentiment (positive, neutral, and negative) in financial news. |
| Approach: | They introduce an entity-level sentiment classification dataset called FinEntity that annotates financial entity spans and their sentiment in financial news. |
| Outcome: | The proposed dataset annotates financial entity spans and their sentiment (positive, neutral, and negative) in financial news. |
Evaluating and Aligning Human Economic Risk Preferences in LLMs (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are increasingly used in decision-making scenarios that involve risk assessment, yet their alignment with human economic rationality remains unclear. |
| Approach: | They propose an evaluation metric called Risk Disparity Score (RDS) and assess whether LLM-generated responses reflect appropriate levels of risk aversion or risk-seeking behavior based on individual’s persona. |
| Outcome: | The proposed evaluation metric assesses whether LLM-generated responses reflect appropriate levels of risk aversion or risk-seeking behavior based on individual’s persona. |
Bridging the Pose-Semantic Gap: A Cascade Framework for Text-Based Person Anomaly Search (2026.findings-acl)
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| Challenge: | Existing methods for person anomaly search fail to address the complexities of real-world security, authors say . Existing approaches fail to detect subtle semantic distinctions, authors argue . |
| Approach: | They propose a framework that decouples retrieval into two stages . structure-aware coarse retrieval and detective squad interaction are proposed . |
| Outcome: | The proposed framework achieves state-of-the-art performance by balancing efficiency and semantic reasoning. |
Reasoning Hijacking: The Fragility of Reasoning Alignment in Large Language Models (2026.acl-long)
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| Challenge: | Current LLM safety research focuses on mitigating **Goal Hijacking**, preventing attackers from redirecting a model’s high-level objective. |
| Approach: | They propose a new adversarial prompt attack paradigm that subverts model judgments by injecting spurious decision criteria without altering the high-level task goal. |
| Outcome: | The proposed model subverts model judgments by injecting spurious decision criteria without altering the high-level task goal. |
Revealing the Numeracy Gap: An Empirical Investigation of Text Embedding Models (2026.findings-eacl)
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| Challenge: | Text embedding models are widely used in natural language processing but are often benchmarked on tasks that do not require understanding nuanced numerical information in text. |
| Approach: | They evaluate 13 widely used text embedding models and find they struggle to capture numerical details accurately. |
| Outcome: | The proposed models struggle to capture nuanced numerical details accurately, despite being benchmarked on tasks that do not require understanding nuance. |
FinMTEB: Finance Massive Text Embedding Benchmark (2025.emnlp-main)
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| Challenge: | Existing text embedding benchmarks for financial domains are inadequately addressing the nuanced requirements of specialized domains like finance. |
| Approach: | They propose a finance-adapted embedding model that outperforms general-purpose models . they also introduce a new model, Fin-E5, which is also open-sourced . |
| Outcome: | The proposed framework outperforms general-purpose models on financial embedding tasks. |
Iterative Self-Correction for Text-Driven Person Re-Identification with Large Vision-Language Models (2026.findings-acl)
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| Challenge: | Existing methods for Person Re-Identification (ReID) adopt a static "one-pass" paradigm, converting images to text once for retrieval. |
| Approach: | They propose a framework that reformulates ReID as an iterative "Think-and-Refine" process. |
| Outcome: | The proposed framework outperforms state-of-the-art methods in complex occlusion scenarios. |
Fine-Grained Data Ordering Improves Fine-Tuning for Large Language Models (2026.findings-acl)
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Xiaomeng Hu, Yixuan Tang, Haoze Li, Hao Chen, Qi Zhang, Zhanming Shen, Yiming Zhang, Haobo Wang, Junbo Zhao
| Challenge: | Prior work focused on data preprocessing, focusing on filtering and cleaning data . a study aimed to improve fine-grained scheduling of data order in epochs . |
| Approach: | They propose a fine-grained scheduling method of data order in epochs to fill this gap . they define data difficulty based on relevance between data and model . |
| Outcome: | The proposed method improves on pre-training and small-scale fine-tuning experiments 2.4% over baselines. |
Uncovering the Bigger Picture: Comprehensive Event Understanding Via Diverse News Retrieval (2025.emnlp-main)
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| Challenge: | Existing news retrieval systems prioritize textual relevance, leading to redundancy and limited viewpoint exposure. |
| Approach: | They propose a framework that explicitly models semantic variation at the sentence level. |
| Outcome: | The proposed framework outperforms strong baselines in terms of retrieval diversity without compromising relevance. |
MPCG: Multi-Round Persona-Conditioned Generation for Modeling the Evolution of Misinformation with LLMs (2025.emnlp-main)
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| Challenge: | Misinformation evolves as it spreads, shifting in language, framing, and moral emphasis to adapt to new audiences. |
| Approach: | They propose a multi-round, persona-conditioned framework that simulates how claims are iteratively reinterpreted by agents with distinct ideological perspectives. |
| Outcome: | The proposed framework generates persona-specific claims across multiple rounds . it is based on an uncensored large language model and is scalable to multiple tasks . |
The Missing Parts: Augmenting Fact Verification with Half Truth Detection (2025.emnlp-main)
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| Challenge: | Existing fact verification systems assess whether a claim is true or false . but many real-world claims are half-truths due to omission of critical context . a new framework that detects omitted information can improve existing fact-checking pipelines . |
| Approach: | They propose a framework that detects omission-based misinformation by aligning evidence and inferring implied intent. |
| Outcome: | The proposed framework boosts Half-True classification F1 by up to 16 points . it can be integrated into existing fact-checking pipelines and improves performance across strong baselines. |
KV-Embedding: Training-free Text Embedding via Internal KV Re-routing in Decoder-only LLMs (2026.acl-long)
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| Challenge: | Recent work shows that decoder-only LLMs can serve as strong embedding backbones when fine-tuned with contrastive objectives. |
| Approach: | They propose a framework that activates the latent representation power of frozen LLMs by rerouting the final token's KV states as a prepended prefix. |
| Outcome: | The proposed framework outperforms existing training-free baselines by 10% on MTEB and maintains robust performance on sequences up to 4,096 tokens. |
Audio-centric Video Understanding Benchmark without Text Shortcut (2025.emnlp-main)
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Yudong Yang, Jimin Zhuang, Guangzhi Sun, Changli Tang, Yixuan Li, Peihan Li, Yifan Jiang, Wei Li, Zejun Ma, Chao Zhang
| Challenge: | Recent advances in multimodal large language models (MLLMs) focus on visual abilities, but audio is essential for video understanding. |
| Approach: | They propose an audio-centric video understanding benchmark to evaluate video comprehension capabilities of multimodal LLMs with a particular focus on auditory information. |
| Outcome: | The proposed video understanding benchmarks evaluate video comprehension capabilities of multimodal models with a particular focus on auditory information. |
Exploring the Relationship between In-Context Learning and Instruction Tuning (2024.findings-emnlp)
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| Challenge: | In-Context Learning (ICL) and Instruction Tuning (IT) are two primary paradigms of adopting Large Language Models (LLMs) to downstream applications, but they are significantly different. |
| Approach: | They examine how the hidden states of Large Language Models change in these two paradigms by examining how they differ in implementation. |
| Outcome: | The proposed model changes the hidden states of LLMs as if its accompanying demonstrations were used to instructionally tune the model. |
FLARE: Task-Agnostic Embedding Model Evaluation via Normalizing Flows (2026.findings-acl)
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| Challenge: | Existing methods based on kernel estimators or Gaussian mixtures fail to model high-dimensional distributions effectively, resulting in unstable rankings. |
| Approach: | They propose a method which uses normalizing flows to estimate information sufficiency in high-dimensional spaces by learning invertible transformations. |
| Outcome: | Experiments on 11 datasets show that FLARE achieves a strong Spearman’s (up to 0.90) with supervised benchmarks, remaining robust even for high-dimensional embeddings (d 3,584). |
Do Multi-Hop Question Answering Systems Know How to Answer the Single-Hop Sub-Questions? (2021.eacl-main)
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| Challenge: | Existing models fail to answer a large portion of sub-questions . Existing systems have achieved super-human performance . |
| Approach: | They propose to use a neural decomposition model to generate sub-questions for a multi-hop question and extract the corresponding sub-answers. |
| Outcome: | The proposed model is based on a hotpotQA dataset with a multi-hop question and sub-answers. |
HyGenar: An LLM-Driven Hybrid Genetic Algorithm for Few-Shot Grammar Generation (2025.findings-acl)
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| Challenge: | Existing grammar generation models perform sub-optimally, resulting in inconsistent syntactic and semantic accuracy. |
| Approach: | They propose an LLM-driven hybrid genetic algorithm to optimize grammar generation by inferring grammars from a set of examples and generated in Backus-Naur Form. |
| Outcome: | The proposed algorithm improves syntactic and semantic accuracy of generated grammars across LLMs. |
Know the Unknown: An Uncertainty-Sensitive Method for LLM Instruction Tuning (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have impressive capabilities but face significant challenges from hallucinations, which arise from insufficient knowledge or context. |
| Approach: | They propose a novel two-stage approach for contextual question answering that enhances LLMs’ ability to recognise their knowledge boundaries while the second reinforces instruction adherence through carefully designed causal prompts. |
| Outcome: | The proposed approach significantly reduces incorrect answers in contextual QA and improves models’ faithfulness to parametric knowledge, mitigating hallucinations in general QA tasks. |