Papers by Zukang Yang
BWLA: Breaking the Barrier of W1AX Post-Training Quantization for LLMs (2026.acl-long)
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| Challenge: | Large language models have driven major progress in NLP, but memory and compute requirements hinder practical deployment. |
| Approach: | They propose a framework that preserves high accuracy while achieving 1-bit weight quantization . the orthogonal-kronecker transformation learns an orthogonale mapping via EM minimization - a new approach to quantization is proposed . |
| Outcome: | The proposed framework achieves 1-bit weight quantization with low activations with low-bit activations. |
CuriousLLM: Elevating Multi-Document Question Answering with LLM-Enhanced Knowledge Graph Reasoning (2025.naacl-industry)
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| Challenge: | Large Language Models (LLMs) have achieved significant success in open-domain question answering, however, they continue to face challenges such as knowledge cutoffs and hallucinations. |
| Approach: | They propose a new mechanism that integrates a curiosity-driven reasoning mechanism into an LLM agent to generate relevant follow-up questions. |
| Outcome: | The proposed enhancement integrates a curiosity-driven reasoning mechanism into an LLM agent, enabling it to generate relevant follow-up questions, thereby guiding the information retrieval process more efficiently. |