Papers by Qika Lin
Self-supervised Quantized Representation for Seamlessly Integrating Knowledge Graphs with Large Language Models (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) are gaining popularity due to their lack of knowledge hallucination and lack of a coherent model. |
| Approach: | They propose a self-supervised quantized representation method to compress KG structural and semantic knowledge into discrete codes that align the format of language sentences. |
| Outcome: | The proposed framework outperforms existing unsupervised methods producing more distinguishable codes on KG link prediction and triple classification tasks. |
A Semantic Mention Graph Augmented Model for Document-Level Event Argument Extraction (2024.lrec-main)
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| Challenge: | Document-level Event Argument Extraction (DEAE) aims to identify arguments and their specific roles from unstructured document. |
| Approach: | They propose a document-prompt-based method for document-level event argument extraction that uses a semantic mention graph to capture relations between documents and prompts. |
| Outcome: | The proposed method surpasses baseline methods and achieves state-of-the-art performance on RAMS and WikiEvents datasets. |
MUR: Momentum Uncertainty guided Reasoning for Large Language Models (2026.acl-long)
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Hang Yan, Fangzhi Xu, Rongman Xu, Yifei Li, Jian Zhang, Haoran Luo, Xiaobao Wu, Anh Tuan Luu, Haiteng Zhao, Qika Lin, Jun Liu
| Challenge: | Existing methods for optimizing reasoning quality are limited by overthinking. |
| Approach: | They propose a method that allocates thinking budgets to critical reasoning steps by tracking and aggregating step-wise uncertainty over time. |
| Outcome: | The proposed method reduces computation by over 45% on average while improving accuracy by 0.33–3.46%. |
TECHS: Temporal Logical Graph Networks for Explainable Extrapolation Reasoning (2023.acl-long)
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| Challenge: | Existing frameworks for extrapolating knowledge graphs are incomplete and do not represent real-world knowledge. |
| Approach: | They propose an explainable extrapolation reasoning framework that integrates propositional reasoning and first-order reasoning by introducing a reasoning graph that iteratively expands to find the answer. |
| Outcome: | The proposed framework outperforms state-of-the-art baselines in explaining future facts based on past counterparts. |
Concept rather than Document: Context Compression via AMR-based Conceptual Entropy (2026.findings-acl)
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| Challenge: | Existing methods rely on lexical or token-level features that fragment semantic units and fail to capture conceptually essential content. |
| Approach: | They propose an unsupervised framework leveraging Abstract Meaning Representation to preserve essential information while filtering irrelevant text. |
| Outcome: | The proposed framework outperforms RAG and existing baselines while preserving essential information while filtering irrelevant text. |
Symbol-LLM: Towards Foundational Symbol-centric Interface For Large Language Models (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) have limitations when it comes to comprehending and expressing world knowledge that extends beyond the boundaries of natural language. |
| Approach: | They propose a model that integrates symbolic data into LLM training without loss of generality ability. |
| Outcome: | The proposed model performs better on symbol- and NL-centric tasks. |
Inductive Relation Prediction with Logical Reasoning Using Contrastive Representations (2022.emnlp-main)
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| Challenge: | Existing methods for relation prediction in knowledge graphs (KGs) are limited by the inductive setting because entities in training process are finite. |
| Approach: | They propose a graph convolutional network-based model LogCo with logical reasoning by contrastive representations that extracts subgraphs and relational paths between two entities to supply the entity-independence. |
| Outcome: | The proposed model outperforms existing methods on twelve inductive datasets. |
PathReasoner: Modeling Reasoning Path with Equivalent Extension for Logical Question Answering (2024.acl-long)
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| Challenge: | Existing logical reasoning tasks are challenging, especially for large language models. |
| Approach: | They propose a logic reasoning task model that transforms each logical sample into reasoning paths and propose an atom extension strategy supported by equivalent logical formulas to form new reasoning paths. |
| Outcome: | The proposed architecture achieves competitive performances on two logical reasoning benchmarks and great generalization abilities. |
MAXS: Meta-Adaptive Exploration with LLM Agents (2026.findings-acl)
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Jian Zhang, Zhiyuan Wang, Zhangqi Wang, Yu He, Haoran Luo, li Yuan, Lingling Zhang, Rui Mao, Qika Lin, Jun Liu
| Challenge: | Existing methods for inference are often myopic and have divergent reasoning paths . a meta-adaptive reasoning framework is proposed to improve the efficiency of LLM agents . |
| Approach: | They propose a meta-adaptive reasoning framework that integrates tool execution and reasoning planning. |
| Outcome: | The proposed framework outperforms existing methods in performance and inference efficiency. |
𝜙-Decoding: Adaptive Foresight Sampling for Balanced Inference-Time Exploration and Exploitation (2025.acl-long)
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| Challenge: | Existing inference-time optimization strategies address the shortsightedness of auto-regressive generation, but the vast search space leads to excessive exploration and insufficient exploitation. |
| Approach: | They propose a decoding strategy that approximates two distributions via foresight and clustering to provide an efficient estimation of step value. |
| Outcome: | The proposed decoding strategy outperforms strong baselines in performance and efficiency. |
InTriage: Intelligent Telephone Triage in Pre-Hospital Emergency Care (2025.emnlp-demos)
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| Challenge: | Existing TT processes face challenges such as incomplete data collection, communication barriers, and manual errors, leading to high over-triage and under-triages rates. |
| Approach: | They propose to use an AI-driven multilingual TT system to provide decision support for triage. |
| Outcome: | The proposed system achieves word error rate of 14.57% for speech recognition and an F1 score of 73.34% for key information extraction. |
Crab: A Novel Configurable Role-Playing LLM with Assessing Benchmark (2025.acl-long)
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Kai He, Yucheng Huang, Wenqing Wang, Delong Ran, Dongming Sheng, Junxuan Huang, Qika Lin, Jiaxing Xu, Wenqiang Liu, Mengling Feng
| Challenge: | Existing RP-LLMs employ only a single role with numerous dialogues, but Crab enables dynamic configuration of desired roles, thereby enhancing related flexibility and adaptability. |
| Approach: | They propose a Configurable Role-Playing LLM with Assessing Benchmark that combines a Role dataset curation, persona-emodying Llm construction, and comprehensive benchmark creation for RP dialogue generation. |
| Outcome: | The proposed model outperforms existing LLMs in performing fine-grained evaluations of RP while keeping dialogue per role minimal. |