Papers by Junchen Zhao
GAP-Gen: Guided Automatic Python Code Generation (2023.eacl-srw)
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| Challenge: | Several previous approaches convert a sentence into a formal statement by mapping verbs to functions in the formal language. |
| Approach: | They propose a Guided Automatic Python Code Generation method based on Python syntactic constraints and semantic constraints. |
| Outcome: | The proposed method achieves better results on automatic Python code generation task than previous methods. |
LinguaLinked: Distributed Large Language Model Inference on Mobile Devices (2024.acl-demos)
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| Challenge: | Recent research shows that large language models demonstrate enhanced capabilities in various language tasks. |
| Approach: | They introduce a system for decentralized, distributed LLM inference on mobile devices . they use optimized model assignment technique to segment LLMs and linear optimization to align segments with each device . |
| Outcome: | The proposed system performs well on high-end to low-end Android devices. |
CoopQ: Cooperative Game Inspired Layerwise Mixed Precision Quantization for LLMs (2026.findings-acl)
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| Challenge: | Existing methods for large language models struggle when the average precision drops below four bits, limiting deployment on resourceconstrained devices such as mobiles, edge sensors, or standard GPUs. |
| Approach: | They propose a game-like game-inspired mixed-precision quantization method which translates these Shapley estimates into a binary quadratic optimization formulation, assigning either 2 or 4-bit precision to layers under strict memory constraints. |
| Outcome: | The proposed method reduces Perplexity by 20 – 80 % across average precisions spanning 4 bit down to 2 bit, compared to methods relying on isolated metrics. |
PCMID: Multi-Intent Detection through Supervised Prototypical Contrastive Learning (2023.findings-emnlp)
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| Challenge: | Existing approaches to intent detection assume that each utterance represents only a single intent. |
| Approach: | They propose a framework for intent detection that can learn multiple representations of a given user utterance under the context of different intent labels in an optimized semantic space. |
| Outcome: | The proposed framework achieves state-of-the-art on multiple public benchmark datasets and a private real-world dataset for the multi-intent detection task. |
Improving Large Language Models via Fine-grained Reinforcement Learning with Minimum Editing Constraint (2024.findings-acl)
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| Challenge: | Existing reinforcement learning methods do not provide fine-grained supervision for complex reasoning tasks. |
| Approach: | They propose a reinforcement learning method that incorporates a generative model as the reward model and a token-level supervision model for RL training. |
| Outcome: | Experiments on 8 tasks show the proposed method is effective . |
SentSim: Crosslingual Semantic Evaluation of Machine Translation (2021.naacl-main)
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| Challenge: | Machine translation (MT) is currently evaluated in one of two ways: monolingually or trained crosslingually by building a supervised model to predict quality scores from human-labeled data. |
| Approach: | They propose an unsupervised model that directly compares the source and machine translated sentence using strong pretrained multilingual word and sentence representations. |
| Outcome: | The proposed model outperforms glass-box approaches to quality estimation that rely on a supervised model. |