Search Results for author:
Found 17 papers, 9 papers with code
SH2: Self-Highlighted Hesitation Helps You Decode More Truthfully
Therefore, we propose to ''highlight'' the factual information by selecting the tokens with the lowest probabilities and concatenating them to the original context, thus forcing the model to repeatedly read and hesitate on these tokens before generation.
MELA: Multilingual Evaluation of Linguistic Acceptability
Recent benchmarks for Large Language Models (LLMs) have mostly focused on application-driven tasks such as complex reasoning and code generation, and this has led to a scarcity in purely linguistic evaluation of LLMs.
Revisiting Acceptability Judgements
We introduce CoLAC - Corpus of Linguistic Acceptability in Chinese, the first large-scale acceptability dataset for a non-Indo-European language.
ArguGPT: evaluating, understanding and identifying argumentative essays generated by GPT models
To address these challenges in English language teaching, we first present ArguGPT, a balanced corpus of 4, 038 argumentative essays generated by 7 GPT models in response to essay prompts from three sources: (1) in-class or homework exercises, (2) TOEFL and (3) GRE writing tasks.
Investigating Transfer Learning in Multilingual Pre-trained Language Models through Chinese Natural Language Inference
These results, however, come with important caveats: cross-lingual models often perform best when trained on a mixture of English and high-quality monolingual NLI data (OCNLI), and are often hindered by automatically translated resources (XNLI-zh).
OCNLI: Original Chinese Natural Language Inference
In this paper, we present the first large-scale NLI dataset (consisting of ~56, 000 annotated sentence pairs) for Chinese called the Original Chinese Natural Language Inference dataset (OCNLI).
CLUE: A Chinese Language Understanding Evaluation Benchmark
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.
MonaLog: a Lightweight System for Natural Language Inference Based on Monotonicity
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.
Probing Natural Language Inference Models through Semantic Fragments
Our experiments, using a library of 8 such semantic fragments, reveal two remarkable findings: (a) State-of-the-art models, including BERT, that are pre-trained on existing NLI benchmark datasets perform poorly on these new fragments, even though the phenomena probed here are central to the NLI task.
Ensemble Methods to Distinguish Mainland and Taiwan Chinese
This paper describes the IUCL system at VarDial 2019 evaluation campaign for the task of discriminating between Mainland and Taiwan variation of mandarin Chinese.
Natural Language Inference with Monotonicity
This paper describes a working system which performs natural language inference using polarity-marked parse trees.
Polarity Computations in Flexible Categorial Grammar
This paper shows how to take parse trees in CCG and algorithmically find the polarities of all the constituents.
Detecting Syntactic Features of Translated Chinese
We present a machine learning approach to distinguish texts translated to Chinese (by humans) from texts originally written in Chinese, with a focus on a wide range of syntactic features.
Path of Vowel Raising in Chengdu Dialect of Mandarin
He and Rao (2013) reported a raising phenomenon of /a/ in /Xan/ (X being a consonant or a vowel) in Chengdu dialect of Mandarin, i. e. /a/ is realized as [epsilon] for young speakers but [ae] for older speakers, but they offered no acoustic analysis.
Is China Entering WTO or shijie maoyi zuzhi--a Corpus Study of English Acronyms in Chinese Newspapers
This is one of the first studies that quantitatively examine the usage of English acronyms (e. g. WTO) in Chinese texts.
Non-Deterministic Segmentation for Chinese Lattice Parsing
Parsing Chinese critically depends on correct word segmentation for the parser since incorrect segmentation inevitably causes incorrect parses.
