activity
20192026
most citedIncorporating Sememes into Chinese Definition Modeling

1 citations · 3 across the 11 of their papers we have counts for

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15 papers · 1 filter

cs.CL2026

PR-CAD: Progressive Refinement for Unified Controllable and Faithful Text-to-CAD Generation with Large Language Models

Jiyuan An, Jiachen Zhao, Fan Chen +6

The construction of CAD models has traditionally relied on labor-intensive manual operations and specialized expertise. Recent advances in large language models (LLMs) have inspire…

cs.CL2026

From Human Cognition to Neural Activations: Probing the Computational Primitives of Spatial Reasoning in LLMs

Jiyuan An, Liner Yang, Mengyan Wang +3

As spatial intelligence becomes an increasingly important capability for foundation models, it remains unclear whether large language models' (LLMs) performance on spatial reasonin…

cs.CL2024

From Text to CQL: Bridging Natural Language and Corpus Search Engine

Luming Lu, Jiyuan An, Yujie Wang +8

Natural Language Processing (NLP) technologies have revolutionized the way we interact with information systems, with a significant focus on converting natural language queries int…

cs.CL2024

Cross-domain Chinese Sentence Pattern Parsing

Jingsi Yu, Cunliang Kong, Liner Yang +6

Sentence Pattern Structure (SPS) parsing is a syntactic analysis method primarily employed in language teaching.Existing SPS parsers rely heavily on textbook corpora for training,…

cs.CL2024

OMGEval: An Open Multilingual Generative Evaluation Benchmark for Large Language Models

Yang Liu, Meng Xu, Shuo Wang +7

Modern large language models (LLMs) should generally benefit individuals from various cultural backgrounds around the world. However, most recent advanced generative evaluation ben…

cs.CL2023

MCTS: A Multi-Reference Chinese Text Simplification Dataset

Ruining Chong, Luming Lu, Liner Yang +6

Text simplification aims to make the text easier to understand by applying rewriting transformations. There has been very little research on Chinese text simplification for a long…