Papers by Jingxiang Chen

2 papers
Evaluating Memory Capability in Continuous Lifelog Scenario (2026.findings-acl)

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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.
Aligning Paralinguistic Understanding and Generation in Speech LLMs via Multi-Task Reinforcement Learning (2026.eacl-industry)

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Challenge: Using paralinguistic cues is challenging for speech large language models, authors say . limited training data, annotation difficulty, and models exploiting lexical shortcuts are challenges . a recent study shows that modeling paralinguistic reasoning with multitask RL improves paralinguistics understanding .
Approach: They propose multi-task reinforcement learning with chain-of-thought prompting that elicits explicit affective reasoning.
Outcome: The proposed model improves paralinguistics understanding over baselines and strong proprietary models by 8-12% on Expresso, IEMOCAP, and RAVDESS.

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