Search Results for author:
Found 16 papers, 9 papers with code
Towards Text-Image Interleaved Retrieval
In this work, we introduce the text-image interleaved retrieval (TIIR) task, where the query and document are interleaved text-image sequences, and the model is required to understand the semantics from the interleaved context for effective retrieval.
GME: Improving Universal Multimodal Retrieval by Multimodal LLMs
Last, we provide in-depth analyses of model scaling and training strategies, and perform ablation studies on both the model and synthetic data.
When Text Embedding Meets Large Language Model: A Comprehensive Survey
Text embedding has become a foundational technology in natural language processing (NLP) during the deep learning era, driving advancements across a wide array of downstream tasks.
Improving General Text Embedding Model: Tackling Task Conflict and Data Imbalance through Model Merging
Recently, the advent of pretrained language models, along with unified benchmarks like the Massive Text Embedding Benchmark (MTEB), has facilitated the development of versatile general-purpose text embedding models.
An End-to-End Model for Photo-Sharing Multi-modal Dialogue Generation
We propose the first end-to-end model for photo-sharing multi-modal dialogue generation, which integrates an image perceptron and an image generator with a large language model.
mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval
We first introduce a text encoder (base size) enhanced with RoPE and unpadding, pre-trained in a native 8192-token context (longer than 512 of previous multilingual encoders).
Technique Report of CVPR 2024 PBDL Challenges
The intersection of physics-based vision and deep learning presents an exciting frontier for advancing computer vision technologies.
Chinese Sequence Labeling with Semi-Supervised Boundary-Aware Language Model Pre-training
Experimental results on Chinese sequence labeling datasets demonstrate that the improved BABERT variant outperforms the vanilla version, not only on these tasks but also more broadly across a range of Chinese natural language understanding tasks.
A Two-Stage Adaptation of Large Language Models for Text Ranking
In this work, we propose a two-stage progressive paradigm to better adapt LLMs to text ranking.
Text Representation Distillation via Information Bottleneck Principle
Pre-trained language models (PLMs) have recently shown great success in text representation field.
Language Models are Universal Embedders
As such cases span from English to other natural or programming languages, from retrieval to classification and beyond, it is desirable to build a unified embedding model rather than dedicated ones for each scenario.
Towards General Text Embeddings with Multi-stage Contrastive Learning
We present GTE, a general-purpose text embedding model trained with multi-stage contrastive learning.
Challenging Decoder helps in Masked Auto-Encoder Pre-training for Dense Passage Retrieval
Recently, various studies have been directed towards exploring dense passage retrieval techniques employing pre-trained language models, among which the masked auto-encoder (MAE) pre-training architecture has emerged as the most promising.
Unsupervised Boundary-Aware Language Model Pretraining for Chinese Sequence Labeling
We apply BABERT for feature induction of Chinese sequence labeling tasks.
Ranked #1 on
Chinese Word Segmentation
on MSRA
Chinese Named Entity Recognition
Chinese Word Segmentation
+4
Retrieval Oriented Masking Pre-training Language Model for Dense Passage Retrieval
Pre-trained language model (PTM) has been shown to yield powerful text representations for dense passage retrieval task.
HLATR: Enhance Multi-stage Text Retrieval with Hybrid List Aware Transformer Reranking
Existing text retrieval systems with state-of-the-art performance usually adopt a retrieve-then-reranking architecture due to the high computational cost of pre-trained language models and the large corpus size.
Ranked #1 on
Passage Re-Ranking
on MS MARCO
