Demo Data from EDBT 2026 / reviewers in the wild / expert
Liuxin Yang
dblp:402/1580
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Language models and text generation › natural language understanding
chinese language understanding |
0.9 | 1 | 2025 | 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 |
0.9 | 1 | 2025 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | CHENGYU-BENCH: Benchmarking Large Language Models for Chinese Idiom Understanding and UseabstractChinese 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 |
EMNLP | 3 |