1 citations · 1 across the 27 of their papers we have counts for
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SpecBlock: Block-Iterative Speculative Decoding with Dynamic Tree Drafting
Weijie Shi, Qiang Xu, Fan Deng +9
Speculative decoding accelerates LLM inference by drafting a tree of candidate continuations and verifying it in one target forward. Existing drafters fall into two camps with oppo…
ACR: Adaptive Context Refactoring via Context Refactoring Operators for Multi-Turn Dialogue
Jiawei Shen, Jia Zhu, Hanghui Guo +9
Large Language Models (LLMs) have shown remarkable performance in multi-turn dialogue. However, in multi-turn dialogue, models still struggle to stay aligned with what has been est…
EduEval: A Hierarchical Cognitive Benchmark for Evaluating Large Language Models in Chinese Education
Guoqing Ma, Jia Zhu, Hanghui Guo +5
Large language models (LLMs) demonstrate significant potential for educational applications. However, their unscrutinized deployment poses risks to educational standards, underscor…
Reading Between the Timelines: RAG for Answering Diachronic Questions
Kwun Hang Lau, Ruiyuan Zhang, Weijie Shi +2
While Retrieval-Augmented Generation (RAG) excels at injecting static, factual knowledge into Large Language Models (LLMs), it exhibits a critical deficit in handling longitudinal…
Semantic-guided Diverse Decoding for Large Language Model
Weijie Shi, Yue Cui, Yaguang Wu +7
Diverse decoding of large language models is crucial for applications requiring multiple semantically distinct responses, yet existing methods primarily achieve lexical rather than…
Enhancing Tool Learning in Large Language Models with Hierarchical Error Checklists
Yue Cui, Liuyi Yao, Shuchang Tao +4
Large language models (LLMs) have significantly advanced natural language processing, particularly through the integration of external tools and APIs. However, their effectiveness…
DioR: Adaptive Cognitive Detection and Contextual Retrieval Optimization for Dynamic Retrieval-Augmented Generation
Hanghui Guo, Jia Zhu, Shimin Di +3
Dynamic Retrieval-augmented Generation (RAG) has shown great success in mitigating hallucinations in large language models (LLMs) during generation. However, existing dynamic RAG m…
Benchmarking Multi-National Value Alignment for Large Language Models
Weijie Shi, Chengyi Ju, Chengzhong Liu +8
Do Large Language Models (LLMs) hold positions that conflict with your country's values? Occasionally they do! However, existing works primarily focus on ethical reviews, failing t…
DIDS: Domain Impact-aware Data Sampling for Large Language Model Training
Weijie Shi, Jipeng Zhang, Yaguang Wu +8
Large language models (LLMs) are commonly trained on multi-domain datasets, where domain sampling strategies significantly impact model performance due to varying domain importance…