Add to ORKGDense retrieval (DR) converts queries and documents into dense embeddings and measures the similarity between queries and documents in vector space. One of the challenges in DR is the lack of domain-specific training data. While DR models can learn from large-scale public datasets like MS MARCO through transfer learning, evidence shows that not all DR models and domains can benefit from transfer learning equally. Recently, some researchers have resorted to large language models (LLMs) to improve the zero-shot and few-shot DR models. However, the hard prompts or human-written prompts utilized in these works cannot guarantee the good quality of generated weak queries. To tackle this, we propose soft prompt tuning for augmenting DR (SPTAR): Fo...
Sparse Mixture-of-Experts (MoE) is a neural architecture design that can be utilized to add learnabl...
Prompt-based learning has been an effective paradigm for large pretrained language models (LLM), ena...
Prompt tuning learns soft prompts to condition frozen Pre-trained Language Models (PLMs) for perform...
Enhancing the zero-shot performance of instruction-following models requires heavy computation, eith...
The pre-training and fine-tuning paradigm has contributed to a number of breakthroughs in Natural La...
In this work, we propose a simple method that applies a large language model (LLM) to large-scale re...
Many information retrieval tasks require large labeled datasets for fine-tuning. However, such datas...
Soft prompts have been recently proposed as a tool for adapting large frozen language models (LMs) t...
Dense retrieval models have predominantly been studied for English, where models have shown great su...
Text retrieval is a long-standing research topic on information seeking, where a system is required ...
Recent work has shown that small distilled language models are strong competitors to models that are...
Pretrained large language models (LLMs) are strong in-context learners that are able to perform few-...
Prompt tuning attempts to update few task-specific parameters in pre-trained models. It has achieved...
Pre-training on larger datasets with ever increasing model size is now a proven recipe for increased...
Despite their recent popularity and well-known advantages, dense retrievers still lag behind sparse ...
Sparse Mixture-of-Experts (MoE) is a neural architecture design that can be utilized to add learnabl...
Prompt-based learning has been an effective paradigm for large pretrained language models (LLM), ena...
Prompt tuning learns soft prompts to condition frozen Pre-trained Language Models (PLMs) for perform...
Enhancing the zero-shot performance of instruction-following models requires heavy computation, eith...
The pre-training and fine-tuning paradigm has contributed to a number of breakthroughs in Natural La...
In this work, we propose a simple method that applies a large language model (LLM) to large-scale re...
Many information retrieval tasks require large labeled datasets for fine-tuning. However, such datas...
Soft prompts have been recently proposed as a tool for adapting large frozen language models (LMs) t...
Dense retrieval models have predominantly been studied for English, where models have shown great su...
Text retrieval is a long-standing research topic on information seeking, where a system is required ...
Recent work has shown that small distilled language models are strong competitors to models that are...
Pretrained large language models (LLMs) are strong in-context learners that are able to perform few-...
Prompt tuning attempts to update few task-specific parameters in pre-trained models. It has achieved...
Pre-training on larger datasets with ever increasing model size is now a proven recipe for increased...
Despite their recent popularity and well-known advantages, dense retrievers still lag behind sparse ...
Sparse Mixture-of-Experts (MoE) is a neural architecture design that can be utilized to add learnabl...
Prompt-based learning has been an effective paradigm for large pretrained language models (LLM), ena...
Prompt tuning learns soft prompts to condition frozen Pre-trained Language Models (PLMs) for perform...