Papers by Yile Chen
Evaluating Memory Capability in Continuous Lifelog Scenario (2026.findings-acl)
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Jianjie Zheng, Zhichen Liu, Zhanyu Shen, Jingxiang Qu, Guanhua Chen, Yile Wang, Yang Xu, Yang Liu, Sijie Cheng
| Challenge: | Existing benchmarks focus on online one-on-one chatting or human-AI interactions, neglecting real-world scenarios. |
| Approach: | They propose a framework to curate a lifelog benchmark that combines two subsets of audio data to address temporal leakage in offline settings. |
| Outcome: | The proposed framework outperforms existing benchmarks on live chats and AI interactions. |
UrbanLLM: Autonomous Urban Activity Planning and Management with Large Language Models (2024.findings-emnlp)
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| Challenge: | UrbanLLM is a fine-tuned large language model designed to tackle diverse urban problems. |
| Approach: | They propose a fine-tuned large language model to tackle diverse urban problems . UrbanLLM decomposes urban-related queries into manageable sub-tasks . |
| Outcome: | The proposed model outperforms existing models in urban planning and management tasks. |
Beyond Majority Voting: Towards Fine-grained and More Reliable Reward Signal for Test-Time Reinforcement Learning (2026.acl-long)
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| Challenge: | RLVR is a paradigm for improving reasoning ability of large language models . but voting results often induce confirmation bias and suffer from sparse rewards . |
| Approach: | They propose a framework integrating model confidence and dynamic subgroup partitioning to address these issues. |
| Outcome: | The proposed framework outperforms recent baselines on multiple models and benchmarks. |
SemPA: Improving Sentence Embeddings of Large Language Models through Semantic Preference Alignment (2026.findings-acl)
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| Challenge: | Existing sentence embedding methods rely on fixed prompt templates or involve modifications to the model architecture, compromising its generative capabilities. |
| Approach: | They propose a sentence-level direct preference optimization approach that boosts the sentence representations while preserving the generative ability of LLMs. |
| Outcome: | The proposed method improves representations of semantically meaningful vectors without sacrificing generation capability. |