123 citations · 335 across the 26 of their papers we have counts for
21 papers · 1 filter
Thinking Augmented Pre-training
Liang Wang, Nan Yang, Shaohan Huang +2
This paper introduces a simple and scalable approach to improve the data efficiency of large language model (LLM) training by augmenting existing text data with thinking trajectori…
Bootstrap Your Own Context Length
Liang Wang, Nan Yang, Xingxing Zhang +2
We introduce a bootstrapping approach to train long-context language models by exploiting their short-context capabilities only. Our method utilizes a simple agent workflow to synt…
Little Giants: Synthesizing High-Quality Embedding Data at Scale
Haonan Chen, Liang Wang, Nan Yang +4
Synthetic data generation has become an increasingly popular way of training models without the need for large, manually labeled datasets. For tasks like text embedding, synthetic…
LongEmbed: Extending Embedding Models for Long Context Retrieval
Dawei Zhu, Liang Wang, Nan Yang +4
Embedding models play a pivot role in modern NLP applications such as IR and RAG. While the context limit of LLMs has been pushed beyond 1 million tokens, embedding models are stil…
Multilingual E5 Text Embeddings: A Technical Report
Liang Wang, Nan Yang, Xiaolong Huang +3
This technical report presents the training methodology and evaluation results of the open-source multilingual E5 text embedding models, released in mid-2023. Three embedding model…
Generative Representational Instruction Tuning
Niklas Muennighoff, Hongjin Su, Liang Wang +5
All text-based language problems can be reduced to either generation or embedding. Current models only perform well at one or the other. We introduce generative representational in…
Improving Text Embeddings with Large Language Models
Liang Wang, Nan Yang, Xiaolong Huang +3
In this paper, we introduce a novel and simple method for obtaining high-quality text embeddings using only synthetic data and less than 1k training steps. Unlike existing methods…
PoSE: Efficient Context Window Extension of LLMs via Positional Skip-wise Training
Dawei Zhu, Nan Yang, Liang Wang +4
Large Language Models (LLMs) are trained with a pre-defined context length, restricting their use in scenarios requiring long inputs. Previous efforts for adapting LLMs to a longer…
Learning to Retrieve In-Context Examples for Large Language Models
Liang Wang, Nan Yang, Furu Wei
Large language models (LLMs) have demonstrated their ability to learn in-context, allowing them to perform various tasks based on a few input-output examples. However, the effectiv…
Learning to Rank in Generative Retrieval
Yongqi Li, Nan Yang, Liang Wang +2
Generative retrieval stands out as a promising new paradigm in text retrieval that aims to generate identifier strings of relevant passages as the retrieval target. This generative…
Multiview Identifiers Enhanced Generative Retrieval
Yongqi Li, Nan Yang, Liang Wang +2
Instead of simply matching a query to pre-existing passages, generative retrieval generates identifier strings of passages as the retrieval target. At a cost, the identifier must b…
Inference with Reference: Lossless Acceleration of Large Language Models
Nan Yang, Tao Ge, Liang Wang +5
We propose LLMA, an LLM accelerator to losslessly speed up Large Language Model (LLM) inference with references. LLMA is motivated by the observation that there are abundant identi…
Text Embeddings by Weakly-Supervised Contrastive Pre-training
Liang Wang, Nan Yang, Xiaolong Huang +5
This paper presents E5, a family of state-of-the-art text embeddings that transfer well to a wide range of tasks. The model is trained in a contrastive manner with weak supervision…
SimKGC: Simple Contrastive Knowledge Graph Completion with Pre-trained Language Models
Liang Wang, Wei Zhao, Zhuoyu Wei +1
Knowledge graph completion (KGC) aims to reason over known facts and infer the missing links. Text-based methods such as KGBERT (Yao et al., 2019) learn entity representations from…
Aligning Cross-lingual Sentence Representations with Dual Momentum Contrast
Liang Wang, Wei Zhao, Jingming Liu
In this paper, we propose to align sentence representations from different languages into a unified embedding space, where semantic similarities (both cross-lingual and monolingual…
Ape210K: A Large-Scale and Template-Rich Dataset of Math Word Problems
Wei Zhao, Mingyue Shang, Yang Liu +2
Automatic math word problem solving has attracted growing attention in recent years. The evaluation datasets used by previous works have serious limitations in terms of scale and d…
Investigating Label Bias in Beam Search for Open-ended Text Generation
Liang Wang, Jinlong Liu, Jingming Liu
Beam search is an effective and widely used decoding algorithm in many sequence-to-sequence (seq2seq) text generation tasks. However, in open-ended text generation, beam search is…
Denoising based Sequence-to-Sequence Pre-training for Text Generation
Liang Wang, Wei Zhao, Ruoyu Jia +2
This paper presents a new sequence-to-sequence (seq2seq) pre-training method PoDA (Pre-training of Denoising Autoencoders), which learns representations suitable for text generatio…
Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data
Wei Zhao, Liang Wang, Kewei Shen +2
Neural machine translation systems have become state-of-the-art approaches for Grammatical Error Correction (GEC) task. In this paper, we propose a copy-augmented architecture for…
Multi-Perspective Context Aggregation for Semi-supervised Cloze-style Reading Comprehension
Liang Wang, Sujian Li, Wei Zhao +4
Cloze-style reading comprehension has been a popular task for measuring the progress of natural language understanding in recent years. In this paper, we design a novel multi-persp…
Yuanfudao at SemEval-2018 Task 11: Three-way Attention and Relational Knowledge for Commonsense Machine Comprehension
Liang Wang, Meng Sun, Wei Zhao +2
This paper describes our system for SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge. We use Three-way Attentive Networks (TriAN) to model interactions betwe…