Papers by Yi Chu
L2Dir: Integrating L_2-Norm and Directional Alignment for Unsupervised Contrastive Representation Learning in Multimodal Retrieval (2026.acl-long)
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Tianyu Zong, Rui Dai, Hongzhu Yi, Yuanxiang Wang, Zhenghao Zhang, Zhenyu Guan, Yujia Yang, Bingkang Shi, Yueyang Ding, Xiangxiang Chu, Kaikui Liu, Jungang Xu
| Challenge: | Existing approaches to multimodal representation learning focus on directional alignment and embedding magnitudes (L2-norm) however, these methods often fail to account for the intrinsic role of L2-norm in the contrastive process. |
| Approach: | They propose a plug-and-play framework that optimizes L2-norm alignment and Directional consistency jointly. |
| Outcome: | The proposed framework achieves consistent and significant performance gains over established baselines across 95 tasks using UniIR and VLM2Vec-V2 frameworks. |
TalkUp: Paving the Way for Understanding Empowering Language (2023.findings-emnlp)
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| Challenge: | Empowerment has rarely been studied in NLP because of its implicit nature . linguistics and psychology research shows how empowerment can impact people by increasing their sense of self-efficacy and self-esteem. |
| Approach: | They crowdsource Reddit posts labeled for empowerment and use it to train language models that capture empowering and disempowering language. |
| Outcome: | The proposed dataset can be used to train language models that capture empowering and disempowering language. |
FeedEval: Pedagogically Aligned Evaluation of LLM-Generated Essay Feedback (2026.findings-acl)
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| Challenge: | Recent research emphasizes the generation of high-quality feedback that provides justification and actionable guidance. |
| Approach: | They propose an LLM-based framework for evaluating LLM feedback along three dimensions: specificity, helpfulness, and validity. |
| Outcome: | The proposed framework evaluates LLM-generated feedback along three dimensions: specificity, helpfulness, and validity. |
Not All Options Are Created Equal: Textual Option Weighting for Token-Efficient LLM-Based Knowledge Tracing (2025.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have strong reasoning and generalization abilities, but they struggle to reflect the histories of example learners within a single prompt during in-context learning. |
| Approach: | They propose a LLM-based option weighted knowledge tracing framework that encodes the interaction histories of example learners in context as textual categorical option weights. |
| Outcome: | The proposed framework outperforms existing LLM-based KT models in warm-start and few-shot settings. |
Joint Modeling of Structure Identification and Nuclearity Recognition in Macro Chinese Discourse Treebank (C18-1)
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| Challenge: | Discourse parsing is a challenging task and plays a critical role in discourse analysis. |
| Approach: | They propose a macro discourse structure presentation schema to present the macro level discourse structure analysis. |
| Outcome: | The proposed corpus is based on two tasks of macro discourse structure analysis, including structure identification and nuclearity recognition. |
How Large Language Models Balance Internal Knowledge with User and Document Assertions (2026.findings-acl)
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| Challenge: | Large language models often need to balance their internal parametric knowledge with external information, such as user beliefs and content from retrieved documents, in real-world scenarios like RAG or chat-based systems. |
| Approach: | They propose a three-source interaction framework to evaluate 27 large language models from 3 families on 2 datasets. |
| Outcome: | The proposed framework systematically evaluates 27 large language models from 3 families on 2 datasets. |
Rationale Behind Essay Scores: Enhancing S-LLM’s Multi-Trait Essay Scoring with Rationale Generated by LLMs (2025.findings-naacl)
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| Challenge: | Existing automated essay scoring relies on essay text without explanatory rationales for the scores. |
| Approach: | They propose a rationale-based multiple trait scoring approach that integrates large language models with a smaller large language model to generate trait-specific rationales. |
| Outcome: | The proposed approach outperforms state-of-the-art models and vanilla S-LLMs on benchmark datasets. |
CGBridge: Bridging Code Graphs and Large Language Models for Better Structure-Aware Code Understanding (2026.findings-acl)
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| Challenge: | Existing structure-aware approaches treat structure as serialized text prompts or auxiliary training objectives, failing to provide explicit guidance during inference. |
| Approach: | They propose a plug-and-play method that enhances Large Language Models with Code Graph information through an external, trainable Bridge module. |
| Outcome: | The proposed method decouples structural reasoning from textual generation without updating the backbone. |
LLaTiSA: Towards Difficulty-Stratified Time Series Reasoning from Visual Perception to Semantics (2026.findings-acl)
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| Challenge: | Current research hinders the development of unified Time Series Reasoning Models (TSRMs) time series data are a fundamental modality for capturing the temporal dynamics of complex systems. |
| Approach: | They propose a time series reasoning model that integrates visualized patterns with precision-calibrated numerical tables to enhance the temporal perception of Vision-Language Models. |
| Outcome: | The proposed model outperforms existing models and exhibits robust out-of-distribution generalization across diverse tasks and real-world scenarios. |