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Found 14 papers, 10 papers with code
From Matching to Generation: A Survey on Generative Information Retrieval
We will summarize the advancements in GR regarding model training, document identifier, incremental learning, downstream tasks adaptation, multi-modal GR and generative recommendation, as well as progress in reliable response generation in aspects of internal knowledge memorization, external knowledge augmentation, generating response with citations and personal information assistant.
Extensible Embedding: A Flexible Multipler For LLM's Context Length
2) Strong sample efficiency of training, which enables the embedding model to be learned in a cost-effective way.
BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation
It can simultaneously perform the three common retrieval functionalities of embedding model: dense retrieval, multi-vector retrieval, and sparse retrieval, which provides a unified model foundation for real-world IR applications.
Flexibly Scaling Large Language Models Contexts Through Extensible Tokenization
Extensible Tokenization stands as a midware in between of the tokenized context and the LLM, which transforms the raw token embeddings into the extensible embeddings.
INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuning
Despite this, their application to information retrieval (IR) tasks is still challenging due to the infrequent occurrence of many IR-specific concepts in natural language.
Soaring from 4K to 400K: Extending LLM's Context with Activation Beacon
Although the context window can be extended through fine-tuning, it will result in a considerable cost at both training and inference time, and exert an unfavorable impact to the LLM's original capabilities.
LM-Cocktail: Resilient Tuning of Language Models via Model Merging
Despite simplicity, LM-Cocktail is surprisingly effective: the resulted model is able to achieve a strong empirical performance in the whole scope of general tasks while preserving a superior capacity in its targeted domain.
Retrieve Anything To Augment Large Language Models
On the other hand, the task-specific retrievers lack the required versatility, hindering their performance across the diverse retrieval augmentation scenarios.
C-Pack: Packaged Resources To Advance General Chinese Embedding
Along with our resources on general Chinese embedding, we release our data and models for English text embeddings.
Generative Retrieval via Term Set Generation
On top of the term-set DocID, we propose a permutation-invariant decoding algorithm, with which the term set can be generated in any permutation yet will always lead to the corresponding document.
Hybrid Inverted Index Is a Robust Accelerator for Dense Retrieval
Based on comprehensive experiments on popular retrieval benchmarks, we verify that clusters and terms indeed complement each other, enabling HI$^2$ to achieve lossless retrieval quality with competitive efficiency across various index settings.
Ultron: An Ultimate Retriever on Corpus with a Model-based Indexer
In order to unify these two stages, we explore a model-based indexer for document retrieval.
GateFormer: Speeding Up News Feed Recommendation with Input Gated Transformers
News feed recommendation is an important web service.
Learning to Select Historical News Articles for Interaction based Neural News Recommendation
The key to personalized news recommendation is to match the user's interests with the candidate news precisely and efficiently.
