39 citations · 82 across the 19 of their papers we have counts for
14 papers · 1 filter
BitNet Text Embeddings
Zhen Li, Xin Huang, Liang Wang +8
LLM-based text embedders have substantially improved retrieval and semantic representation quality, but their deployment remains costly: large backbone models slow down embedding i…
Only Say What You Know: Calibration-Aware Generation for Long-Form Factuality
Wen Luo, Guangyue Peng, Liang Wang +7
Large Reasoning Models achieve strong performance on complex tasks but remain prone to hallucinations, particularly in long-form generation where errors compound across reasoning s…
Learning to Draft: Adaptive Speculative Decoding with Reinforcement Learning
Jiebin Zhang, Zhenghan Yu, Liang Wang +8
Speculative decoding accelerates large language model (LLM) inference by using a small draft model to generate candidate tokens for a larger target model to verify. The efficacy of…
Two Pathways to Truthfulness: On the Intrinsic Encoding of LLM Hallucinations
Wen Luo, Guangyue Peng, Wei Li +8
Despite their impressive capabilities, large language models (LLMs) frequently generate hallucinations. Previous work shows that their internal states encode rich signals of truthf…
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…
Examining False Positives under Inference Scaling for Mathematical Reasoning
Yu Wang, Nan Yang, Liang Wang +2
Recent advancements in language models have led to significant improvements in mathematical reasoning across various benchmarks. However, most of these benchmarks rely on automatic…
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…
Pronunciation Assessment with Multi-modal Large Language Models
Kaiqi Fu, Linkai Peng, Nan Yang +1
Large language models (LLMs), renowned for their powerful conversational abilities, are widely recognized as exceptional tools in the field of education, particularly in the contex…
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…