109 citations · 283 across the 21 of their papers we have counts for
21 papers
Competition-Level Problems are Effective LLM Evaluators
Yiming Huang, Zhenghao Lin, Xiao Liu +8
Large language models (LLMs) have demonstrated impressive reasoning capabilities, yet there is ongoing debate about these abilities and the potential data contamination problem rec…
EIPE-text: Evaluation-Guided Iterative Plan Extraction for Long-Form Narrative Text Generation
Wang You, Wenshan Wu, Yaobo Liang +8
Plan-and-Write is a common hierarchical approach in long-form narrative text generation, which first creates a plan to guide the narrative writing. Following this approach, several…
GameEval: Evaluating LLMs on Conversational Games
Dan Qiao, Chenfei Wu, Yaobo Liang +2
The rapid advancements in large language models (LLMs) have presented challenges in evaluating those models. Existing evaluation methods are either reference-based or preference ba…
Analyzing and Reducing the Performance Gap in Cross-Lingual Transfer with Fine-tuning Slow and Fast
Yiduo Guo, Yaobo Liang, Dongyan Zhao +2
Existing research has shown that a multilingual pre-trained language model fine-tuned with one (source) language also performs well on downstream tasks for non-source languages, ev…
Machine-Created Universal Language for Cross-lingual Transfer
Yaobo Liang, Quanzhi Zhu, Junhe Zhao +1
There are two primary approaches to addressing cross-lingual transfer: multilingual pre-training, which implicitly aligns the hidden representations of various languages, and trans…
Learning to Plan with Natural Language
Yiduo Guo, Yaobo Liang, Chenfei Wu +3
Large Language Models (LLMs) have shown remarkable performance in various basic natural language tasks. For completing the complex task, we still need a plan for the task to guide…
AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models
Wanjun Zhong, Ruixiang Cui, Yiduo Guo +6
Evaluating the general abilities of foundation models to tackle human-level tasks is a vital aspect of their development and application in the pursuit of Artificial General Intell…
TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs
Yaobo Liang, Chenfei Wu, Ting Song +11
Artificial Intelligence (AI) has made incredible progress recently. On the one hand, advanced foundation models like ChatGPT can offer powerful conversation, in-context learning an…
Modeling Sequential Sentence Relation to Improve Cross-lingual Dense Retrieval
Shunyu Zhang, Yaobo Liang, Ming Gong +2
Recently multi-lingual pre-trained language models (PLM) such as mBERT and XLM-R have achieved impressive strides in cross-lingual dense retrieval. Despite its successes, they are…
Unsupervised Context Aware Sentence Representation Pretraining for Multi-lingual Dense Retrieval
Ning Wu, Yaobo Liang, Houxing Ren +4
Recent research demonstrates the effectiveness of using pretrained language models (PLM) to improve dense retrieval and multilingual dense retrieval. In this work, we present a sim…
Cross-Lingual Ability of Multilingual Masked Language Models: A Study of Language Structure
Yuan Chai, Yaobo Liang, Nan Duan
Multilingual pre-trained language models, such as mBERT and XLM-R, have shown impressive cross-lingual ability. Surprisingly, both of them use multilingual masked language model (M…
Multi-View Document Representation Learning for Open-Domain Dense Retrieval
Shunyu Zhang, Yaobo Liang, Ming Gong +2
Dense retrieval has achieved impressive advances in first-stage retrieval from a large-scale document collection, which is built on bi-encoder architecture to produce single vector…
XLM-K: Improving Cross-Lingual Language Model Pre-training with Multilingual Knowledge
Xiaoze Jiang, Yaobo Liang, Weizhu Chen +1
Cross-lingual pre-training has achieved great successes using monolingual and bilingual plain text corpora. However, most pre-trained models neglect multilingual knowledge, which i…
Discovering Representation Sprachbund For Multilingual Pre-Training
Yimin Fan, Yaobo Liang, Alexandre Muzio +4
Multilingual pre-trained models have demonstrated their effectiveness in many multilingual NLP tasks and enabled zero-shot or few-shot transfer from high-resource languages to low…
Simpson's Bias in NLP Training
Fei Yuan, Longtu Zhang, Huang Bojun +1
In most machine learning tasks, we evaluate a model on a given data population by measuring a population-level metric . Examples of such evaluation metric inclu…
Tag and Correct: Question aware Open Information Extraction with Two-stage Decoding
Martin Kuo, Yaobo Liang, Lei Ji +4
Question Aware Open Information Extraction (Question aware Open IE) takes question and passage as inputs, outputting an answer tuple which contains a subject, a predicate, and one…
GLOW : Global Weighted Self-Attention Network for Web Search
Xuan Shan, Chuanjie Liu, Yiqian Xia +6
Deep matching models aim to facilitate search engines retrieving more relevant documents by mapping queries and documents into semantic vectors in the first-stage retrieval. When l…
XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation
Yaobo Liang, Nan Duan, Yeyun Gong +21
In this paper, we introduce XGLUE, a new benchmark dataset that can be used to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora and evalu…
Document Modeling with Graph Attention Networks for Multi-grained Machine Reading Comprehension
Bo Zheng, Haoyang Wen, Yaobo Liang +5
Natural Questions is a new challenging machine reading comprehension benchmark with two-grained answers, which are a long answer (typically a paragraph) and a short answer (one or…
Enhancing Answer Boundary Detection for Multilingual Machine Reading Comprehension
Fei Yuan, Linjun Shou, Xuanyu Bai +5
Multilingual pre-trained models could leverage the training data from a rich source language (such as English) to improve performance on low resource languages. However, the transf…
Unicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks
Haoyang Huang, Yaobo Liang, Nan Duan +4
We present Unicoder, a universal language encoder that is insensitive to different languages. Given an arbitrary NLP task, a model can be trained with Unicoder using training data…