Papers by Zhihong Jin

3 papers
CycleOIE: A Low-Resource Training Framework For Open Information Extraction (2025.coling-main)

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Challenge: Open Information Extraction (OpenIE) models rely heavily on large amounts of annotated data.
Approach: They propose a training framework that maximizes data efficiency through a cycle-consistency mechanism.
Outcome: The proposed approach improves the quality of training data by curating low-quality datasets annotated by a large language model.
LOOK-M: Look-Once Optimization in KV Cache for Efficient Multimodal Long-Context Inference (2024.findings-emnlp)

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Challenge: Long-context Multimodal Large Language Models (MLLMs) require substantial computational resources for inference . the growth of their multimodal Key-Value (KV) cache challenges memory and time efficiency.
Approach: They propose a fine-tuning-free approach that efficiently reduces the multimodal KV cache size while maintaining performance comparable to a full cache.
Outcome: The proposed method reduces the multimodal KV cache size while maintaining performance comparable to a full cache.
Enhancing Code Generation Performance of Smaller Models by Distilling the Reasoning Ability of LLMs (2024.lrec-main)

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Challenge: Large Language Models (LLMs) have made significant advances in code generation through the ‘Chain-of-Thought’ prompting technique.
Approach: They propose a framework which aims to transfer LLMs’ reasoning capabilities to smaller models through distillation.
Outcome: The proposed framework improves the smaller model's code generation performance by over 130% on the APPS benchmark.

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