43 research outputs found
EcomGPT: Instruction-tuning Large Language Model with Chain-of-Task Tasks for E-commerce
Recently, instruction-following Large Language Models (LLMs) , represented by
ChatGPT, have exhibited exceptional performance in general Natural Language
Processing (NLP) tasks. However, the unique characteristics of E-commerce data
pose significant challenges to general LLMs. An LLM tailored specifically for
E-commerce scenarios, possessing robust cross-dataset/task generalization
capabilities, is a pressing necessity. To solve this issue, in this work, we
proposed the first e-commerce instruction dataset EcomInstruct, with a total of
2.5 million instruction data. EcomInstruct scales up the data size and task
diversity by constructing atomic tasks with E-commerce basic data types, such
as product information, user reviews. Atomic tasks are defined as intermediate
tasks implicitly involved in solving a final task, which we also call
Chain-of-Task tasks. We developed EcomGPT with different parameter scales by
training the backbone model BLOOMZ with the EcomInstruct. Benefiting from the
fundamental semantic understanding capabilities acquired from the Chain-of-Task
tasks, EcomGPT exhibits excellent zero-shot generalization capabilities.
Extensive experiments and human evaluations demonstrate that EcomGPT
outperforms ChatGPT in term of cross-dataset/task generalization on E-commerce
tasks.Comment: Initial version of EcomGP
Improving Text Matching in E-Commerce Search with A Rationalizable, Intervenable and Fast Entity-Based Relevance Model
Discovering the intended items of user queries from a massive repository of
items is one of the main goals of an e-commerce search system. Relevance
prediction is essential to the search system since it helps improve
performance. When online serving a relevance model, the model is required to
perform fast and accurate inference. Currently, the widely used models such as
Bi-encoder and Cross-encoder have their limitations in accuracy or inference
speed respectively. In this work, we propose a novel model called the
Entity-Based Relevance Model (EBRM). We identify the entities contained in an
item and decompose the QI (query-item) relevance problem into multiple QE
(query-entity) relevance problems; we then aggregate their results to form the
QI prediction using a soft logic formulation. The decomposition allows us to
use a Cross-encoder QE relevance module for high accuracy as well as cache QE
predictions for fast online inference. Utilizing soft logic makes the
prediction procedure interpretable and intervenable. We also show that
pretraining the QE module with auto-generated QE data from user logs can
further improve the overall performance. The proposed method is evaluated on
labeled data from e-commerce websites. Empirical results show that it achieves
promising improvements with computation efficiency
SeqGPT: An Out-of-the-box Large Language Model for Open Domain Sequence Understanding
Large language models (LLMs) have shown impressive ability for open-domain
NLP tasks. However, LLMs are sometimes too footloose for natural language
understanding (NLU) tasks which always have restricted output and input format.
Their performances on NLU tasks are highly related to prompts or demonstrations
and are shown to be poor at performing several representative NLU tasks, such
as event extraction and entity typing. To this end, we present SeqGPT, a
bilingual (i.e., English and Chinese) open-source autoregressive model
specially enhanced for open-domain natural language understanding. We express
all NLU tasks with two atomic tasks, which define fixed instructions to
restrict the input and output format but still ``open'' for arbitrarily varied
label sets. The model is first instruction-tuned with extremely fine-grained
labeled data synthesized by ChatGPT and then further fine-tuned by 233
different atomic tasks from 152 datasets across various domains. The
experimental results show that SeqGPT has decent classification and extraction
ability, and is capable of performing language understanding tasks on unseen
domains. We also conduct empirical studies on the scaling of data and model
size as well as on the transfer across tasks. Our model is accessible at
https://github.com/Alibaba-NLP/SeqGPT.Comment: Initial version of SeqGP
Health System Barriers and Facilitators to Delivering Additional Vaccines through the National Immunisation Programme in China: A Qualitative Study of Provider and Service-User Perspectives.
In China, there are two categories of vaccines available from the Chinese Center for Disease Control and associated public health agencies. Extended Program of Immunization (EPI) vaccines are government-funded and non-EPI vaccines are voluntary and paid for out-of-pocket. The government plans to transition some non-EPI vaccines to EPI in the coming years, which may burden public health system capacity, particularly in terms of budget, workforce, supply chains, and information systems. Our study explored vaccinator and caregiver perspectives on introducing non-EPI vaccines into routine immunization and perceived facilitators and barriers affecting this transition. We conducted a qualitative study from a realist perspective, analysing semi-structured interviews with 26 vaccination providers and 160 caregivers in three provinces, selected to represent regional socioeconomic disparities across Eastern, Central, and Western China. Data were analysed thematically, using deductive and inductive coding. Most participants were positive about adding vaccines to the national schedule. Candidate EPI vaccines most frequently recommended by participants were varicella, mumps vaccine, and hand-foot-mouth disease. Providers generally considered existing workspaces, cold-chain equipment, and funding sufficient, but described frontline staffing and vaccine information systems as requiring improvement. This is the first qualitative study to explore interest, barriers, and facilitators related to adding vaccines to China's national schedule from provider and caregiver perspectives. Findings can inform government efforts to introduce additional vaccines, by including efforts to retain and recruit vaccine programme staff and implement whole-process data management and health information systems that allow unified nationwide data collection and sharing
Global Retinoblastoma Presentation and Analysis by National Income Level.
Importance: Early diagnosis of retinoblastoma, the most common intraocular cancer, can save both a child's life and vision. However, anecdotal evidence suggests that many children across the world are diagnosed late. To our knowledge, the clinical presentation of retinoblastoma has never been assessed on a global scale. Objectives: To report the retinoblastoma stage at diagnosis in patients across the world during a single year, to investigate associations between clinical variables and national income level, and to investigate risk factors for advanced disease at diagnosis. Design, Setting, and Participants: A total of 278 retinoblastoma treatment centers were recruited from June 2017 through December 2018 to participate in a cross-sectional analysis of treatment-naive patients with retinoblastoma who were diagnosed in 2017. Main Outcomes and Measures: Age at presentation, proportion of familial history of retinoblastoma, and tumor stage and metastasis. Results: The cohort included 4351 new patients from 153 countries; the median age at diagnosis was 30.5 (interquartile range, 18.3-45.9) months, and 1976 patients (45.4%) were female. Most patients (n = 3685 [84.7%]) were from low- and middle-income countries (LMICs). Globally, the most common indication for referral was leukocoria (n = 2638 [62.8%]), followed by strabismus (n = 429 [10.2%]) and proptosis (n = 309 [7.4%]). Patients from high-income countries (HICs) were diagnosed at a median age of 14.1 months, with 656 of 666 (98.5%) patients having intraocular retinoblastoma and 2 (0.3%) having metastasis. Patients from low-income countries were diagnosed at a median age of 30.5 months, with 256 of 521 (49.1%) having extraocular retinoblastoma and 94 of 498 (18.9%) having metastasis. Lower national income level was associated with older presentation age, higher proportion of locally advanced disease and distant metastasis, and smaller proportion of familial history of retinoblastoma. Advanced disease at diagnosis was more common in LMICs even after adjusting for age (odds ratio for low-income countries vs upper-middle-income countries and HICs, 17.92 [95% CI, 12.94-24.80], and for lower-middle-income countries vs upper-middle-income countries and HICs, 5.74 [95% CI, 4.30-7.68]). Conclusions and Relevance: This study is estimated to have included more than half of all new retinoblastoma cases worldwide in 2017. Children from LMICs, where the main global retinoblastoma burden lies, presented at an older age with more advanced disease and demonstrated a smaller proportion of familial history of retinoblastoma, likely because many do not reach a childbearing age. Given that retinoblastoma is curable, these data are concerning and mandate intervention at national and international levels. Further studies are needed to investigate factors, other than age at presentation, that may be associated with advanced disease in LMICs