Papers by Juhao Liang

2 papers
Second Language (Arabic) Acquisition of LLMs via Progressive Vocabulary Expansion (2025.acl-long)

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Challenge: In the evolving landscape of large language models, the predominant focus has been on English and Chinese.
Approach: They propose to utilize Arabic-specific vocabulary in the tokenizer to accelerate decoding.
Outcome: The proposed model achieves decent performance comparable to the best Arabic LLMs across various Arabic benchmarks.
Smurfs: Multi-Agent System using Context-Efficient DFSDT for Tool Planning (2025.naacl-long)

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Challenge: Teaching large language models to use tools for solving complex problems can grant them human-like reasoning abilities.
Approach: They propose a multi-agent system that enhances the Deep First Search Decision Tree (DFSDT) to address issues like error propagation and limited exploration in ReAct .
Outcome: The proposed system reduces token usage by 60.9% compared to existing methods and performs on par with GPT-4-DFSDT.

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