Papers by Yifei Guo
Chain of Ideas: Revolutionizing Research Via Novel Idea Development with LLM Agents (2025.findings-emnlp)
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Long Li, Weiwen Xu, Jiayan Guo, Ruochen Zhao, Xingxuan Li, Yuqian Yuan, Boqiang Zhang, Yuming Jiang, Yifei Xin, Ronghao Dang, Yu Rong, Deli Zhao, Tian Feng, Lidong Bing
| Challenge: | Existing methods for idea generation either trivially prompt LLMs or expose LLM to extensive literature without indicating useful information. |
| Approach: | They propose a chain-of-ideas agent that organizes literature in a chains structure . they propose evaluating idea-generation methods from different perspectives . |
| Outcome: | The proposed agent outperforms existing methods and matches human quality in idea generation. |
Locomo-Plus: Beyond-Factual Cognitive Memory Evaluation Framework for LLM Agents (2026.acl-long)
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| Challenge: | Existing benchmarks and evaluation protocols focus on surface-level factual recall. |
| Approach: | They propose a benchmark for assessing cognitive memory under cue–trigger semantic disconnect. |
| Outcome: | The proposed framework reveals failures not captured by existing benchmarks. |
Neural Parameter Search for Slimmer Fine-Tuned Models and Better Transfer (2025.acl-long)
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Guodong Du, Zitao Fang, Jing Li, Junlin Li, Runhua Jiang, Shuyang Yu, Yifei Guo, Yangneng Chen, Sim Kuan Goh, Ho-Kin Tang, Daojing He, Honghai Liu, Min Zhang
| Challenge: | Foundational models and their checkpoints have advanced deep learning, boosting performance across applications. |
| Approach: | They propose a method for pruning fine-tuned models by calculating differences between them and original model. |
| Outcome: | The proposed method can improve performance across vision, NLP, and multi-modal benchmarks. |
UCFE: A User-Centric Financial Expertise Benchmark for Large Language Models (2025.findings-naacl)
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Yuzhe Yang, Yifei Zhang, Yan Hu, Yilin Guo, Ruoli Gan, Yueru He, Mingcong Lei, Xiao Zhang, Haining Wang, Qianqian Xie, Jimin Huang, Honghai Yu, Benyou Wang
| Challenge: | Recent advances in large language models (LLMs) have expanded their potential applications in finance. |
| Approach: | They propose a framework to evaluate the ability of large language models to handle financial tasks using human expert evaluations and task-specific interactions. |
| Outcome: | The proposed framework evaluates the ability of large language models to handle complex financial tasks and combines human expert evaluations with dynamic, task-specific interactions to simulate the complexities of evolving financial scenarios. |
To See a World in a Spark of Neuron: Disentangling Multi-Task Interference for Training-Free Model Merging (2025.emnlp-main)
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Zitao Fang, Guodong Du, Shuyang Yu, Yifei Guo, Yiwei Zhang, Yiyao Cao, Jing Li, Ho-Kin Tang, Sim Kuan Goh
| Challenge: | Existing approaches to model merging ignore the fundamental roles of neurons, connectivity and activation. |
| Approach: | They propose a framework that relies on neuronal mechanisms to mitigate task interference . they decomposed task-specific representations into two complementary subspaces . their results offer new insights into mitigating task interference and improving knowledge fusion . |
| Outcome: | The proposed framework reduces task interference within neurons and improves knowledge fusion. |
AlphaContext: An Evolutionary Tree-based Psychometric Context Generator for Creativity Assessment (2026.acl-long)
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Yixuan Wang, Yue Huang, Hong Qian, Yunzhao Wei, Yifei Ding, Wenkai Wang, Zhi Liu, Zhongjing Huang, Aimin Zhou, Jiajun Guo
| Challenge: | Existing LLM-based tools struggle with insufficient assessment cues, weak narrative coherence, limited stylistic diversity, and poor support for creative thinking. |
| Approach: | They propose an evolutionary tree-based psychometric context generator that integrates rule-guided outline planning, sentence-level MCTS generation, MAP-Elites quality-diversity optimization and assessment-guide refiner simulation. |
| Outcome: | The proposed tool outperforms strong LLMs and structured frameworks on 7 evaluation dimensions and shows higher alignment with expert-designed contexts. |
From General Reward to Targeted Reward: Improving Open-ended Long-context Generation Models (2025.emnlp-main)
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| Challenge: | Current research on long-form context in Large Language Models (LLMs) focuses on understanding of long-contexts, but the open-ended Long Text Generation (Open-LTG) remains underexplored. |
| Approach: | They propose a method that uses data synthesis and a reward signal to enhance model performance. |
| Outcome: | The proposed method outperforms GPT-4-Turbo and improves performance by 20% on the Open-LTG task. |
Semantic-Eval : A Semantic Comprehension Evaluation Framework for Large Language Models Generation without Training (2025.acl-long)
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| Challenge: | Large language models (LLMs) have emerged as key drivers of progress in the field of natural language processing. |
| Approach: | They propose a framework that assesses LLM-generated text based on semantic understanding. |
| Outcome: | The proposed framework surpasses traditional evaluation metrics and lags behind GPT-4. |
Knowledge Fusion By Evolving Weights of Language Models (2024.findings-acl)
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| Challenge: | Experimental results on mainstream language models show that Evolver outperforms previous state-of-the-art models by large margins due to the high training costs of large language models. |
| Approach: | They propose a method to integrate multiple models from diverse training scenarios into a unified model. |
| Outcome: | The proposed method outperforms state-of-the-art models on mainstream language models by large margins. |
FedLFC: Towards Efficient Federated Multilingual Modeling with LoRA-based Language Family Clustering (2024.findings-naacl)
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| Challenge: | Existing frameworks for multilingual modeling face communication costs and parameter interference conflicts. |
| Approach: | They propose a communication-efficient federated learning framework with low-rank adaptation and language family clustering for Multilingual Modeling (MM) they maintain the weights of the base model, updating the lightweight Low-rank adapt parameters to minimize communication costs. |
| Outcome: | The proposed model outperforms the baseline models in performance and reduces communication overhead. |
An Effective Post-training Embedding Binarization Approach for Fast Online Top-K Passage Matching (2022.aacl-short)
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| Challenge: | Existing models that learn semantic representations of passages are prone to performance degradation . embedding binarization is a promising branch of model compression . |
| Approach: | They propose an embedding binarization approach that can be used to optimize for online inference. |
| Outcome: | The proposed model can perform query-passage matching acceleration. |