Papers by Ziqi Zhao

5 papers
Reinforced Efficient Reasoning via Semantically Diverse Exploration (2026.acl-long)

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Challenge: Existing methods for reinforcement learning with verifiable rewards suffer from limited exploration diversity and inefficient reasoning.
Approach: They propose a method that rewards concise and correct reasoning while penalizing unnecessarily long reasoning chains.
Outcome: Extensive experiments on Qwen and Llama models validate the effectiveness and efficiency of ROSE.
APB-V: Accelerating Long-Video Understanding via Sequence-Parallelism-aware Approximate Attention (2026.acl-long)

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Challenge: Existing methods for long-video inference use compression or sparse attention . existing methods restrict LMMs from handling longer, more complex videos .
Approach: They propose a sequence-parallel framework with optimized attention that accelerates long-video inference across multiple GPUs.
Outcome: The proposed framework delivers speedups of 12.72x, 1.70x, and 1.18x over FlashAttn, ZigZagRing, and APB without significant performance loss.
Generalizing Few-Shot Named Entity Recognizers to Unseen Domains with Type-Related Features (2023.findings-emnlp)

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Challenge: Few-shot named entity recognition methods struggle with out-of-domain (OOD) examples due to their reliance on manual labeling for the target domain.
Approach: They propose a framework to enable generalization to an unseen target domain with only a few labeled examples.
Outcome: The proposed framework achieves significant performance improvements on in-domain and cross-domain datasets.
Chinese MentalBERT: Domain-Adaptive Pre-training on Social Media for Chinese Mental Health Text Analysis (2024.findings-acl)

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Challenge: Existing models for language analysis are inadequate for specialized domains like psychology.
Approach: They have enriched a Chinese social media database with psychological lexicons to enhance its applicability to psychological text analysis.
Outcome: The proposed model performed better on six public datasets and provided relevant predictions given the masked sentences.
Unlocking General Long Chain-of-Thought Reasoning Capabilities of Large Language Models via Representation Engineering (2025.acl-long)

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Challenge: Existing work finds that long CoT reasoning can be efficiently elicited by tuning on only a few examples and can easily transfer to other tasks.
Approach: They propose a representation engineering method to unleash the general long CoT reasoning capabilities of LLMs.
Outcome: The proposed method is effective in in-domain and cross-domain scenarios.

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