Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Liuxin Yang

dblp:402/1580 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Language models and text generation · 61% Knowledge representation and reasoning · 30% Information extraction and text analysis · 9%

Topics — the 2 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Language models and text generation › natural language understanding
chinese language understanding
0.912025
CHENGYU-BENCH: Benchmarking Large Language Models for Chinese Idiom Understanding and Use · EMNLP 2025
Knowledge, reasoning and agents › Knowledge representation and reasoning › pragmatics
idiom understanding
0.912025
CHENGYU-BENCH: Benchmarking Large Language Models for Chinese Idiom Understanding and Use · EMNLP 2025

Methods — techniques the papers use, named apart from their topics

open cloze · 0.9multiple-choice cloze · 0.9large language model evaluation · 0.9
YearPublicationVenuePosition
2025 CHENGYU-BENCH: Benchmarking Large Language Models for Chinese Idiom Understanding and Use
abstract
Chinese idioms (成语, Chengyu) are concise four-character expressions steeped in history and culture, whose literal translations often fail to capture their full meaning.This complexity makes them challenging for language models to interpret and use correctly.Existing benchmarks focus on narrow tasks-multiplechoice cloze tests, isolated translation, or simple paraphrasing.We introduce CHENGYU-BENCH, a comprehensive benchmark featuring three tasks: (1) Evaluative Connotation, classifying idioms as positive or negative; (2) Appropriateness, detecting incorrect idiom usage in context; and (3) Open Cloze, filling blanks in longer passages without options.CHENGYU-BENCH comprises 2,937 human-verified examples covering 1,765 common idioms sourced from diverse corpora.We evaluate leading LLMs and find they achieve over 95% accuracy on Evaluative Connotation, but only ~85% on Appropriateness and ~40% top-1 accuracy on Open Cloze.Error analysis reveals that most mistakes arise from fundamental misunderstandings of idiom meanings.CHENGYU-BENCH demonstrates that while LLMs can reliably gauge idiom sentiment, they still struggle to grasp the cultural and contextual nuances essential for proper usage.The benchmark and source code are available at: https: //github.com/sofyc/ChengyuBench.
Yicheng Fu, Zhemin Huang 0001, Liuxin Yang, Yumeng Lu, Zhongdongming Dai
EMNLP3