Papers by Kunpeng Liu
Analyze Like a Venture Capitalist: Information-Gain and Knowledge Enhanced Graph Reasoning for Startup Success Prediction (2026.findings-acl)
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| Challenge: | Most venture capital investments fail, while a few deliver outsized returns. |
| Approach: | They propose a framework that synthesizes relational evidence across sources . they propose combining information-gain-driven retriever and knowledge base to ground reasoning . |
| Outcome: | The proposed framework achieves +5.9% F1 and +22.1% Precision@5 over state-of-the-art baselines. |
Blind Spot Navigation in Large Language Model Reasoning with Thought Space Explorer (2026.findings-eacl)
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Jinghan Zhang, Fengran Mo, Tharindu Cyril Weerasooriya, Xinyue Ye, Dongjie Wang, Yanjie Fu, Kunpeng Liu
| Challenge: | Existing studies show that large language models have strong reasoning capabilities through chain-structured methods. |
| Approach: | They propose a framework for navigating and expanding thought structures to overcome blind spots in LLM reasoning. |
| Outcome: | The proposed framework overcomes blind spots in large language models by expanding thought structures . the proposed framework improves accuracy of the final answer and intermediate reasoning steps . |
Ensemble Privacy Defense for Knowledge-Intensive LLMs against Membership Inference Attacks (2026.findings-eacl)
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| Challenge: | Large language models (LLMs) are the foundation of modern natural language processing, powering applications across diverse domains. |
| Approach: | They propose a model-agnostic defense framework which aggregates and evaluates the outputs of a knowledge-injected LLM, a base LLM and a dedicated judge model to enhance resistance against membership inference attacks. |
| Outcome: | The proposed framework reduces MIA success by up to 27.8% for SFT and 526.3% for RAG compared to inference-time baseline while maintaining answer quality. |
Diversity-oriented Data Augmentation with Large Language Models (2025.acl-long)
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| Challenge: | Existing data augmentation methods focus on increasing sample numbers while neglecting sample distribution diversity, which can lead to model overfitting. |
| Approach: | They propose a data augmentation framework that focuses on sample distribution diversity and trains a large language model as a diverse paraphraser. |
| Outcome: | The proposed framework achieves an average performance gain of 10.52% surpassing the runner-up baseline with more than three percentage points. |
Prototypical Reward Network for Data-Efficient Model Alignment (2024.acl-long)
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| Challenge: | Reinforcement Learning from Human Feedback (RLHF) is a reward model that fine-tunes Large Language Models (LLMs) by utilizing Prototypical Networks. |
| Approach: | They propose a framework utilizing Prototypical Networks to enhance reward models under limited human feedback, enabling more stable and reliable structural learning from fewer samples. |
| Outcome: | The proposed framework improves reward models under limited human feedback, surpassing traditional methods, especially in data-limited scenarios. |
Entropy-based Exploration Conduction for Multi-step Reasoning (2025.findings-acl)
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| Challenge: | Existing methods to automatically decide the depth of exploration of the reasoning procedure lead to high cost and a lack of flexibility. |
| Approach: | They propose a method that dynamically adjusts the exploration depth during multi-step reasoning by monitoring LLM’s output entropy and variance entropic. |
| Outcome: | The proposed method captures the uncertainty of the current step and the fluctuation of uncertainty across consecutive reasoning steps and then selects whether to deepen, expand, or stop exploration according to the probability. |