Zobrazeno 1 - 10
of 116
pro vyhledávání: '"Lipani, Aldo"'
Recently, researchers have investigated the capabilities of Large Language Models (LLMs) for generative recommender systems. Existing LLM-based recommender models are trained by adding user and item IDs to a discrete prompt template. However, the dis
Externí odkaz:
http://arxiv.org/abs/2407.05033
Instruction tuning plays a crucial role in shaping the outputs of language models (LMs) to desired styles. In this work, we propose a simple yet effective method, Instruction Modelling (IM), which trains LMs by applying a loss function to the instruc
Externí odkaz:
http://arxiv.org/abs/2405.14394
Sentence transformers are language models designed to perform semantic search. This study investigates the capacity of sentence transformers, fine-tuned on general question-answering datasets for asymmetric semantic search, to associate descriptions
Externí odkaz:
http://arxiv.org/abs/2404.04169
Recent state-of-the-art recommender systems predominantly rely on either implicit or explicit feedback from users to suggest new items. While effective in recommending novel options, many recommender systems often use uninterpretable embeddings to re
Externí odkaz:
http://arxiv.org/abs/2402.05810
Conversational agents have become ubiquitous in assisting with daily tasks, and are expected to possess human-like features. One such feature is lexical entrainment (LE), a phenomenon in which speakers in human-human conversations tend to naturally a
Externí odkaz:
http://arxiv.org/abs/2310.09651
Autor:
Shi, Zhengxiang, Lipani, Aldo
Prompt tuning (PT), where a small amount of trainable soft (continuous) prompt vectors is affixed to the input of language models (LM), has shown promising results across various tasks and models for parameter-efficient fine-tuning (PEFT). PT stands
Externí odkaz:
http://arxiv.org/abs/2309.05173
Foundation Industries (FIs) constitute glass, metals, cement, ceramics, bulk chemicals, paper, steel, etc. and provide crucial, foundational materials for a diverse set of economically relevant industries: automobiles, machinery, construction, househ
Externí odkaz:
http://arxiv.org/abs/2308.16089
Dialogue systems and large language models (LLMs) have gained considerable attention. However, the direct utilization of LLMs as task-oriented dialogue (TOD) models has been found to underperform compared to smaller task-specific models. Nonetheless,
Externí odkaz:
http://arxiv.org/abs/2306.09821
Autor:
Shi, Zhengxiang, Lipani, Aldo
In recent years, language models (LMs) have made remarkable progress in advancing the field of natural language processing (NLP). However, the impact of data augmentation (DA) techniques on the fine-tuning (FT) performance of these LMs has been a top
Externí odkaz:
http://arxiv.org/abs/2306.07664
Session-based recommendation, which aims to predict the next item of users' interest as per an existing sequence interaction of items, has attracted growing applications of Contrastive Learning (CL) with improved user and item representations. Howeve
Externí odkaz:
http://arxiv.org/abs/2306.01266