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20222026
most citedMindLLM: Pre-training Lightweight Large Language Model from Scratch, Evaluations and Domain Applications

3 citations · 3 across the 6 of their papers we have counts for

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cs.CL2026

How Far Are We? Systematic Evaluation of LLMs vs. Human Experts in Mathematical Contest in Modeling

Yuhang Liu, Heyan Huang, Yizhe Yang +3

Large language models (LLMs) have achieved strong performance on reasoning benchmarks, yet their ability to solve real-world problems requiring end-to-end workflows remains unclear…

cs.CL2023

Graph vs. Sequence: An Empirical Study on Knowledge Forms for Knowledge-Grounded Dialogue

Yizhe Yang, Heyan Huang, Yihang Liu +1

Knowledge-grounded dialogue is a task of generating an informative response based on both the dialogue history and external knowledge source. In general, there are two forms of kno…

cs.CL2023

PSST: A Benchmark for Evaluation-driven Text Public-Speaking Style Transfer

Huashan Sun, Yixiao Wu, Yuhao Ye +4

Language style is necessary for AI systems to understand and generate diverse human language accurately. However, previous text style transfer primarily focused on sentence-level d…

cs.CL2023★ 3 cited

MindLLM: Pre-training Lightweight Large Language Model from Scratch, Evaluations and Domain Applications

Yizhe Yang, Huashan Sun, Jiawei Li +5

Large Language Models (LLMs) have demonstrated remarkable performance across various natural language tasks, marking significant strides towards general artificial intelligence. Wh…

cs.CL2022

Ask to Understand: Question Generation for Multi-hop Question Answering

Jiawei Li, Mucheng Ren, Yang Gao +1

Multi-hop Question Answering (QA) requires the machine to answer complex questions by finding scattering clues and reasoning from multiple documents. Graph Network (GN) and Questio…