62 citations · 153 across the 17 of their papers we have counts for
18 papers
The First ChineseBabyLM Challenge: training data-efficient and cognitively plausible language models for Chinese
Siyuan Song, Zhiheng Qian, Yunhao Zhang +11
This paper presents the first ChineseBabyLM Challenge, organized as part of NLPCC 2026. The challenge asked participants to train language models from scratch using no more than 10…
Reasoning or Memorization: Can LLMs Understand and Generate Chinese Xiehouyu Riddles?
Hai Hu, Siyuan Song, Chongtian Shao +3
In this paper, we push the boundary of LLM reasoning by testing them in a Chinese language game, xiehouyu, with novel xiehouyu created by linguists that had not existed before to a…
Phonetic forced alignment for low-resource language varieties: Model training and evaluation on Chengdu Mandarin
Zhiheng Qian, Aini Li, Hai Hu +1
Phonetic forced alignment is a key technique in phonetic research, yet existing alignment systems lack specialized models for low-resource language varieties. We address this by tr…
LLMs for automatic annotation of Mandarin narrative transcripts
Qingwen Zhao, Hongao Zhu, Yunqi He +3
Linguistic annotation of transcribed speech is essential for research in language acquisition, language disorders, and sociolinguistics, yet remains labor-intensive and time-consum…
Math Natural Language Inference: this should be easy!
Valeria de Paiva, Qiyue Gao, Hai Hu +4
We ask whether contemporary LLMs are able to perform natural language inference (NLI) tasks on mathematical texts. We call this the Math NLI problem. We construct a corpus of Math…
Translationese-index: Using Likelihood Ratios for Graded and Generalizable Measurement of Translationese
Yikang Liu, Wanyang Zhang, Yiming Wang +6
Translationese refers to linguistic properties that usually occur in translated texts. Previous works study translationese by framing it as a binary classification between original…
Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction
Xinmeng Hou, Lingyue Fu, Chenhao Meng +3
Aspect-Opinion Pair Extraction (AOPE) and Aspect Sentiment Triplet Extraction (ASTE) have drawn growing attention in NLP. However, most existing approaches extract aspects and opin…
A Systematic Assessment of Language Models with Linguistic Minimal Pairs in Chinese
Yikang Liu, Yeting Shen, Hongao Zhu +9
We present ZhoBLiMP, the largest linguistic minimal pair benchmark for Chinese, with over 100 paradigms, ranging from topicalization to the \textit{Ba} construction. We then train…
Do Large Language Models Understand Conversational Implicature -- A case study with a chinese sitcom
Shisen Yue, Siyuan Song, Xinyuan Cheng +1
Understanding the non-literal meaning of an utterance is critical for large language models (LLMs) to become human-like social communicators. In this work, we introduce SwordsmanIm…
SH2: Self-Highlighted Hesitation Helps You Decode More Truthfully
Jushi Kai, Tianhang Zhang, Hai Hu +1
Large language models (LLMs) demonstrate great performance in text generation. However, LLMs are still suffering from hallucinations. In this work, we propose an inference-time met…
MELA: Multilingual Evaluation of Linguistic Acceptability
Ziyin Zhang, Yikang Liu, Weifang Huang +3
In this work, we present the largest benchmark to date on linguistic acceptability: Multilingual Evaluation of Linguistic Acceptability -- MELA, with 46K samples covering 10 langua…
Revisiting Acceptability Judgements
Hai Hu, Ziyin Zhang, Weifang Huang +7
In this work, we revisit linguistic acceptability in the context of large language models. We introduce CoLAC - Corpus of Linguistic Acceptability in Chinese, the first large-scale…
ArguGPT: evaluating, understanding and identifying argumentative essays generated by GPT models
Yikang Liu, Ziyin Zhang, Wanyang Zhang +5
AI generated content (AIGC) presents considerable challenge to educators around the world. Instructors need to be able to detect such text generated by large language models, eithe…
FewCLUE: A Chinese Few-shot Learning Evaluation Benchmark
Liang Xu, Xiaojing Lu, Chenyang Yuan +8
Pretrained Language Models (PLMs) have achieved tremendous success in natural language understanding tasks. While different learning schemes -- fine-tuning, zero-shot, and few-shot…
OCNLI: Original Chinese Natural Language Inference
Hai Hu, Kyle Richardson, Liang Xu +3
Despite the tremendous recent progress on natural language inference (NLI), driven largely by large-scale investment in new datasets (e.g., SNLI, MNLI) and advances in modeling, mo…
CLUE: A Chinese Language Understanding Evaluation Benchmark
Liang Xu, Hai Hu, Xuanwei Zhang +29
The advent of natural language understanding (NLU) benchmarks for English, such as GLUE and SuperGLUE allows new NLU models to be evaluated across a diverse set of tasks. These com…
MonaLog: a Lightweight System for Natural Language Inference Based on Monotonicity
Hai Hu, Qi Chen, Kyle Richardson +3
We present a new logic-based inference engine for natural language inference (NLI) called MonaLog, which is based on natural logic and the monotonicity calculus. In contrast to exi…
Probing Natural Language Inference Models through Semantic Fragments
Kyle Richardson, Hai Hu, Lawrence S. Moss +1
Do state-of-the-art models for language understanding already have, or can they easily learn, abilities such as boolean coordination, quantification, conditionals, comparatives, an…