1,264 research outputs found
Universal Self-adaptive Prompting
A hallmark of modern large language models (LLMs) is their impressive general
zero-shot and few-shot abilities, often elicited through prompt-based and/or
in-context learning. However, while highly coveted and being the most general,
zero-shot performances in LLMs are still typically weaker due to the lack of
guidance and the difficulty of applying existing automatic prompt design
methods in general tasks when ground-truth labels are unavailable. In this
study, we address this by presenting Universal Self-adaptive Prompting (USP),
an automatic prompt design approach specifically tailored for zero-shot
learning (while compatible with few-shot). Requiring only a small amount of
unlabeled data & an inference-only LLM, USP is highly versatile: to achieve
universal prompting, USP categorizes a possible NLP task into one of the three
possible task types, and then uses a corresponding selector to select the most
suitable queries & zero-shot model-generated responses as
pseudo-demonstrations, thereby generalizing ICL to the zero-shot setup in a
fully automated way. We evaluate zero-shot USP with two PaLM models, and
demonstrate performances that are considerably stronger than standard zero-shot
baselines and are comparable to or even superior than few-shot baselines across
more than 20 natural language understanding (NLU) and natural language
generation (NLG) tasks.Comment: 10 pages, 3 figures, 4 tables (19 pages, 5 figures and 9 tables
including references and appendices
GitTables: A Large-Scale Corpus of Relational Tables
The success of deep learning has sparked interest in improving relational
table tasks, like data preparation and search, with table representation models
trained on large table corpora. Existing table corpora primarily contain tables
extracted from HTML pages, limiting the capability to represent offline
database tables. To train and evaluate high-capacity models for applications
beyond the Web, we need resources with tables that resemble relational database
tables. Here we introduce GitTables, a corpus of 1M relational tables extracted
from GitHub. Our continuing curation aims at growing the corpus to at least 10M
tables. Analyses of GitTables show that its structure, content, and topical
coverage differ significantly from existing table corpora. We annotate table
columns in GitTables with semantic types, hierarchical relations and
descriptions from Schema.org and DBpedia. The evaluation of our annotation
pipeline on the T2Dv2 benchmark illustrates that our approach provides results
on par with human annotations. We present three applications of GitTables,
demonstrating its value for learned semantic type detection models, schema
completion methods, and benchmarks for table-to-KG matching, data search, and
preparation. We make the corpus and code available at
https://gittables.github.io
The Life Cycle of Knowledge in Big Language Models: A Survey
Knowledge plays a critical role in artificial intelligence. Recently, the
extensive success of pre-trained language models (PLMs) has raised significant
attention about how knowledge can be acquired, maintained, updated and used by
language models. Despite the enormous amount of related studies, there still
lacks a unified view of how knowledge circulates within language models
throughout the learning, tuning, and application processes, which may prevent
us from further understanding the connections between current progress or
realizing existing limitations. In this survey, we revisit PLMs as
knowledge-based systems by dividing the life circle of knowledge in PLMs into
five critical periods, and investigating how knowledge circulates when it is
built, maintained and used. To this end, we systematically review existing
studies of each period of the knowledge life cycle, summarize the main
challenges and current limitations, and discuss future directions.Comment: paperlist: https://github.com/c-box/KnowledgeLifecycl
VNHSGE: VietNamese High School Graduation Examination Dataset for Large Language Models
The VNHSGE (VietNamese High School Graduation Examination) dataset, developed
exclusively for evaluating large language models (LLMs), is introduced in this
article. The dataset, which covers nine subjects, was generated from the
Vietnamese National High School Graduation Examination and comparable tests.
300 literary essays have been included, and there are over 19,000
multiple-choice questions on a range of topics. The dataset assesses LLMs in
multitasking situations such as question answering, text generation, reading
comprehension, visual question answering, and more by including both textual
data and accompanying images. Using ChatGPT and BingChat, we evaluated LLMs on
the VNHSGE dataset and contrasted their performance with that of Vietnamese
students to see how well they performed. The results show that ChatGPT and
BingChat both perform at a human level in a number of areas, including
literature, English, history, geography, and civics education. They still have
space to grow, though, especially in the areas of mathematics, physics,
chemistry, and biology. The VNHSGE dataset seeks to provide an adequate
benchmark for assessing the abilities of LLMs with its wide-ranging coverage
and variety of activities. We intend to promote future developments in the
creation of LLMs by making this dataset available to the scientific community,
especially in resolving LLMs' limits in disciplines involving mathematics and
the natural sciences.Comment: 74 pages, 44 figure
Current and Future Challenges in Knowledge Representation and Reasoning
Knowledge Representation and Reasoning is a central, longstanding, and active
area of Artificial Intelligence. Over the years it has evolved significantly;
more recently it has been challenged and complemented by research in areas such
as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl
Perspectives workshop was held on Knowledge Representation and Reasoning. The
goal of the workshop was to describe the state of the art in the field,
including its relation with other areas, its shortcomings and strengths,
together with recommendations for future progress. We developed this manifesto
based on the presentations, panels, working groups, and discussions that took
place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge
Representation: its origins, goals, milestones, and current foci; its relation
to other disciplines, especially to Artificial Intelligence; and on its
challenges, along with key priorities for the next decade
Introduction to Psychology
Introduction to Psychology is a modified version of Psychology 2e - OpenStax
PEJL: A path-enhanced joint learning approach for knowledge graph completion
Knowledge graphs (KGs) often suffer from incompleteness. Knowledge graph completion (KGC) is proposed to complete missing components in a KG. Most KGC methods focus on direct relations and fail to leverage rich semantic information in multi-hop paths. In contrast, path-based embedding methods can capture path information and utilize extra semantics to improve KGC. However, most path-based methods cannot take advantage of full multi-hop information and neglect to capture multiple semantic associations between single and multi-hop triples. To bridge the gap, we propose a novel path-enhanced joint learning approach called PEJL for KGC. Rather than learning multi-hop representations, PEJL can recover multi-hop embeddings by encoding full multi-hop components. Meanwhile, PEJL extends the definition of translation energy functions and generates new semantic representations for each multi-hop component, which is rarely considered in path-based methods. Specifically, we first use the path constraint resource allocation (PCRA) algorithm to extract multi-hop triples. Then we use an embedding recovering module consisting of a bidirectional gated recurrent unit (GRU) layer and a fully connected layer to obtain multi-hop embeddings. Next, we employ a KG modeling module to leverage various semantic information and model the whole knowledge graph based on translation methods. Finally, we define a joint learning approach to train our proposed PEJL. We evaluate our model on two KGC datasets: FB15K-237 and NELL-995. Experiments show the effectiveness and superiority of PEJL
Answering Ambiguous Questions via Iterative Prompting
In open-domain question answering, due to the ambiguity of questions,
multiple plausible answers may exist. To provide feasible answers to an
ambiguous question, one approach is to directly predict all valid answers, but
this can struggle with balancing relevance and diversity. An alternative is to
gather candidate answers and aggregate them, but this method can be
computationally costly and may neglect dependencies among answers. In this
paper, we present AmbigPrompt to address the imperfections of existing
approaches to answering ambiguous questions. Specifically, we integrate an
answering model with a prompting model in an iterative manner. The prompting
model adaptively tracks the reading process and progressively triggers the
answering model to compose distinct and relevant answers. Additionally, we
develop a task-specific post-pretraining approach for both the answering model
and the prompting model, which greatly improves the performance of our
framework. Empirical studies on two commonly-used open benchmarks show that
AmbigPrompt achieves state-of-the-art or competitive results while using less
memory and having a lower inference latency than competing approaches.
Additionally, AmbigPrompt also performs well in low-resource settings. The code
are available at: https://github.com/sunnweiwei/AmbigPrompt.Comment: To be published in ACL 202
Predicate Matrix: an interoperable lexical knowledge base for predicates
183 p.La Matriz de Predicados (Predicate Matrix en inglés) es un nuevo recurso léxico-semántico resultado de la integración de múltiples fuentes de conocimiento, entre las cuales se encuentran FrameNet, VerbNet, PropBank y WordNet. La Matriz de Predicados proporciona un léxico extenso y robusto que permite mejorar la interoperabilidad entre los recursos semánticos mencionados anteriormente. La creación de la Matriz de Predicados se basa en la integración de Semlink y nuevos mappings obtenidos utilizando métodos automáticos que enlazan el conocimiento semántico a nivel léxico y de roles. Asimismo, hemos ampliado la Predicate Matrix para cubrir los predicados nominales (inglés, español) y predicados en otros idiomas (castellano, catalán y vasco). Como resultado, la Matriz de predicados proporciona un léxico multilingüe que permite el análisis semántico interoperable en múltiples idiomas
The Knowledge Graph Construction in the Educational Domain: Take an Australian School Science Course as an Example
The evolution of the Internet technology and artificial intelligence has changed the ways we gain knowledge, which has expanded to every aspect of our lives. In recent years, Knowledge Graphs technology as one of the artificial intelligence techniques has been widely used in the educational domain. However, there are few studies dedicating the construction of knowledge graphs for K-10 education in Australia, and most of the existing studies only focus on at the theory level, and little research shows practical pipeline steps to complete the complex flow of constructing the educational knowledge graph. Apart from that, most studies focused on concept entities and their relations but ignored the features of concept entities and the relations between learning knowledge points and required learning outcomes. To overcome these shortages and provide the data foundation for the development of downstream research and applications in this educational domain, the construction processes of building a knowledge graph for Australian K-10 education were analyzed at the theory level and implemented in a practical way in this research. We took the Year 9 science course as a typical data source example fed to the proposed method called K10EDU-RCF-KG to construct this educational knowledge graph and to enrich the features of entities in the knowledge graph. In the construction pipeline, a variety of techniques were employed to complete the building process. Firstly, the POI and OCR techniques were applied to convert Word and PDF format files into text, followed by developing an educational resources management platform where the machine-readable text could be stored in a relational database management system. Secondly, we designed an architecture framework as the guidance of the construction pipeline. According to this architecture, the educational ontology was initially designed, and a backend microservice was developed to process the entity extraction and relation extraction by NLP-NER and probabilistic association rule mining algorithms, respectively. We also adopted the NLP-POS technique to find out the neighbor adjectives related to entitles to enrich features of these concept entitles. In addition, a subject dictionary was introduced during the refinement process of the knowledge graph, which reduced the data noise rate of the knowledge graph entities. Furthermore, the connections between learning outcome entities and topic knowledge point entities were directly connected, which provides a clear and efficient way to identify what corresponding learning objectives are related to the learning unit. Finally, a set of REST APIs for querying this educational knowledge graph were developed
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