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Found 7 papers, 2 papers with code
Speeding up Transformer Decoding via an Attention Refinement Network
Extensive experiments on ten WMT machine translation tasks show that the proposed model yields an average of 1. 35x faster (with almost no decrease in BLEU) over the state-of-the-art inference implementation.
TenTrans High-Performance Inference Toolkit for WMT2021 Efficiency Task
The paper describes the TenTrans’s submissions to the WMT 2021 Efficiency Shared Task.
Alleviating LLM-based Generative Retrieval Hallucination in Alipay Search
Generative retrieval (GR) has revolutionized document retrieval with the advent of large language models (LLMs), and LLM-based GR is gradually being adopted by the industry.
Test-time Computing: from System-1 Thinking to System-2 Thinking
In System-2 models, it enhances the model's reasoning ability to solve complex problems through repeated sampling, self-correction, and tree search.
Boosting LLM-based Relevance Modeling with Distribution-Aware Robust Learning
In this paper, we propose a novel Distribution-Aware Robust Learning framework (DaRL) for relevance modeling in Alipay Search.
CPRM: A LLM-based Continual Pre-training Framework for Relevance Modeling in Commercial Search
Our CPRM framework includes three modules: 1) employing both queries and multi-field item to jointly pre-train for enhancing domain knowledge, 2) applying in-context pre-training, a novel approach where LLMs are pre-trained on a sequence of related queries or items, and 3) conducting reading comprehension on items to produce associated domain knowledge and background information (e. g., generating summaries and corresponding queries) to further strengthen LLMs.
Towards Boosting LLMs-driven Relevance Modeling with Progressive Retrieved Behavior-augmented Prompting
In response, we propose ProRBP, a novel Progressive Retrieved Behavior-augmented Prompting framework for integrating search scenario-oriented knowledge with LLMs effectively.
