Papers by Xuanqing Yu
ExpNote: Black-box Large Language Models are better Task Solvers with Experience Notebook (2023.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have shown great power in solving various tasks but fail in many specific tasks. |
| Approach: | They propose a framework to help black-box LLMs better adapt to unfamiliar tasks by reflecting and noting experiences from training data and retrieving them from external memory during testing. |
| Outcome: | The proposed framework improves the performance of black-box Large Language Models on multiple tasks and demonstrates that it is a good choice for the future. |
Shuttle Between Symbolic Instructions and Neural Parameters of Large Language Models (2026.acl-long)
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| Challenge: | Despite their distinct external representations, a deeper analysis reveals their intrinsic nature: instructions serve as a natural language compression devised by humans for data governing specific mapping patterns, whereas parameters act as 'neuro compression' of the same task data. |
| Approach: | They propose a neural network framework to model and learn the bi-directional mappings between instructions and parameters of large language models by evaluating it on the tasks of instruction deduction and induction. |
| Outcome: | The proposed framework can map one of the instructions/parameters to the other by evaluating it on the tasks of instruction deduction and induction. |
ItD: Large Language Models Can Teach Themselves Induction through Deduction (2024.acl-long)
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| Challenge: | Recent studies have shown that Large Language Models (LLMs) have limited ability to conduct induction. |
| Approach: | They propose a framework to enable LLMs to teach themselves induction through deduction. |
| Outcome: | The proposed framework improves performance on two induction benchmarks and shows that it can be used to teach induction through deduction. |
ONSEP: A Novel Online Neural-Symbolic Framework for Event Prediction Based on Large Language Model (2024.findings-acl)
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| Challenge: | TKGF is a technique that requires experience during testing and relying on a single short-term history. |
| Approach: | They propose a framework that integrates dynamic causal rule mining and dual history augmented generation to enhance event prediction. |
| Outcome: | The proposed framework shows significant performance improvements across diverse datasets and significantly improves Hit@k. |
TiMem: Temporal-Hierarchical Memory Consolidation for Long-Horizon Conversational Agents (2026.findings-acl)
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Kai Li, Xuanqing Yu, Ziyi Ni, Yi Zeng, Yao Xu, Zheqing Zhang, Xin Li, Jitao Sang, Xiaogang Duan, Xuelei Wang, Chengbao Liu, Jie Tan
| Challenge: | Existing memory frameworks provide limited support for temporally structured information across hierarchical levels, leading to fragmented memories and unstable long-horizon personalization. |
| Approach: | They propose a temporal–hierarchical memory framework that organizes conversations through a Temporal Memory Tree. |
| Outcome: | The proposed framework outperforms baselines while reducing the recalled memory length by 52.20%. |