Related papers: Err

Related papers

Exploring Large Language Models for Product Attribute Value Identification [25.890927969633196]
Product attribute value identification (PAVI) involves automatically identifying attributes and their values from product information. Existing methods rely on fine-tuning pre-trained language models, such as BART and T5. This paper explores large language models (LLMs), such as LLaMA and Mistral, as data-efficient and robust alternatives for PAVI.
arXiv Detail & Related papers (2024-09-19T12:09:33Z)
Using LLMs for the Extraction and Normalization of Product Attribute Values [47.098255866050835]
This paper explores the potential of using large language models (LLMs) to extract and normalize attribute values from product titles and descriptions. We introduce the Web Data Commons - Product Attribute Value Extraction (WDC-PAVE) benchmark dataset for our experiments.
arXiv Detail & Related papers (2024-03-04T15:39:59Z)
JPAVE: A Generation and Classification-based Model for Joint Product Attribute Prediction and Value Extraction [59.94977231327573]
We propose a multi-task learning model with value generation/classification and attribute prediction called JPAVE. Two variants of our model are designed for open-world and closed-world scenarios. Experimental results on a public dataset demonstrate the superiority of our model compared with strong baselines.
arXiv Detail & Related papers (2023-11-07T18:36:16Z)
AE-smnsMLC: Multi-Label Classification with Semantic Matching and Negative Label Sampling for Product Attribute Value Extraction [42.79022954630978]
Product attribute value extraction plays an important role for many real-world applications in e-Commerce such as product search and recommendation. Previous methods treat it as a sequence labeling task that needs more annotation for position of values in the product text. We propose a classification model with semantic matching and negative label sampling for attribute value extraction.
arXiv Detail & Related papers (2023-10-11T02:22:28Z)
Product Information Extraction using ChatGPT [69.12244027050454]
This paper explores the potential of ChatGPT for extracting attribute/value pairs from product descriptions. Our results show that ChatGPT achieves a performance similar to a pre-trained language model but requires much smaller amounts of training data and computation for fine-tuning.
arXiv Detail & Related papers (2023-06-23T09:30:01Z)
A Unified Generative Approach to Product Attribute-Value Identification [6.752749933406399]
We explore a generative approach to the product attribute-value identification (PAVI) task. We finetune a pre-trained generative model, T5, to decode a set of attribute-value pairs as a target sequence from the given product text. Experimental results confirm that our generation-based approach outperforms the existing extraction and classification-based methods.
arXiv Detail & Related papers (2023-06-09T00:33:30Z)
AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators [98.11286353828525]
GPT-3.5 series models have demonstrated remarkable few-shot and zero-shot ability across various NLP tasks. We propose AnnoLLM, which adopts a two-step approach, explain-then-annotate. We build the first conversation-based information retrieval dataset employing AnnoLLM.
arXiv Detail & Related papers (2023-03-29T17:03:21Z)
What Makes Good In-Context Examples for GPT-$3$? [101.99751777056314]
GPT-$3$ has attracted lots of attention due to its superior performance across a wide range of NLP tasks. Despite its success, we found that the empirical results of GPT-$3$ depend heavily on the choice of in-context examples. In this work, we investigate whether there are more effective strategies for judiciously selecting in-context examples.
arXiv Detail & Related papers (2021-01-17T23:38:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.