Papers by Zhihao Jia
DDO: Dual-Decision Optimization for LLM-Based Medical Consultation via Multi-Agent Collaboration (2025.emnlp-main)
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| Challenge: | Existing LLMs fail to capture the dual nature of medical consultation (MC) this mismatch often results in ineffective symptom inquiry and unreliable disease diagnosis. |
| Approach: | They propose a novel LLM-based framework that performs Dual-Decision Optimization by decoupling the two sub-tasks and optimizing them with distinct objectives through a collaborative multi-agent workflow. |
| Outcome: | The proposed framework outperforms existing LLM-based approaches on three real-world MC datasets and achieves competitive performance with state-of-the-art generation-based methods. |
Internal Value Alignment in Large Language Models through Controlled Value Vector Activation (2025.acl-long)
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| Challenge: | Existing LLMs do not possess consistent values, but many have been developed to align them at the behavioral level, including supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF). |
| Approach: | They propose a Controlled Value Vector Activation method that directly aligns the internal values of Large Language Models by interpreting how a value is encoded in their latent representations. |
| Outcome: | The proposed method achieves highest success rate across 10 basic values without hurting model performance and fluency, and ensures target values even with opposite and potentially malicious input prompts. |
Quantized Side Tuning: Fast and Memory-Efficient Tuning of Quantized Large Language Models (2024.acl-long)
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Zhengxin Zhang, Dan Zhao, Xupeng Miao, Gabriele Oliaro, Zhihao Zhang, Qing Li, Yong Jiang, Zhihao Jia
| Challenge: | Existing methods to finetun large language models (LLMs) only update a small number of trainable parameters, or attempt to reduce the memory footprint during the training phase of the finetune process. |
| Approach: | They propose quantized side tuing (QST) which quantizes an LLM’s model weights into 4-bit to reduce the memory footprint of the original weights. |
| Outcome: | The proposed method reduces the memory footprint of the model weights, optimizer states, and intermediate activations while reducing the memory requirements. |