4,429 research outputs found
Effidit: Your AI Writing Assistant
In this technical report, we introduce Effidit (Efficient and Intelligent
Editing), a digital writing assistant that facilitates users to write
higher-quality text more efficiently by using artificial intelligence (AI)
technologies. Previous writing assistants typically provide the function of
error checking (to detect and correct spelling and grammatical errors) and
limited text-rewriting functionality. With the emergence of large-scale neural
language models, some systems support automatically completing a sentence or a
paragraph. In Effidit, we significantly expand the capacities of a writing
assistant by providing functions in five categories: text completion, error
checking, text polishing, keywords to sentences (K2S), and cloud input methods
(cloud IME). In the text completion category, Effidit supports generation-based
sentence completion, retrieval-based sentence completion, and phrase
completion. In contrast, many other writing assistants so far only provide one
or two of the three functions. For text polishing, we have three functions:
(context-aware) phrase polishing, sentence paraphrasing, and sentence
expansion, whereas many other writing assistants often support one or two
functions in this category. The main contents of this report include major
modules of Effidit, methods for implementing these modules, and evaluation
results of some key methods.Comment: Technical report for Effidit. arXiv admin note: text overlap with
arXiv:2202.0641
Best Practices for Evaluating Flight Deck Interfaces for Transport Category Aircraft with Particular Relevance to Issues of Attention, Awareness, and Understanding CAST SE-210 Output 2 Report 6 of 6
Attention, awareness, and understanding of the flight crew are a critical contributor to safety and the flight deck plays a critical role in supporting these cognitive functions. Changes to the flight deck need to be evaluated for whether the changed device provides adequate support for these functions. This report describes a set of diverse evaluation methods. The report recommends designing the interface-evaluation to span the phases of the device development, from early to late, and it provides methods appropriate at each phase. It describes the various ways in which an interface or interface component can fail to support awareness as potential issues to be assessed in evaluation. It summarizes appropriate methods to evaluate different issues concerning inadequate support for these functions, throughout the phases of development
Graph Meets LLMs: Towards Large Graph Models
Large models have emerged as the most recent groundbreaking achievements in
artificial intelligence, and particularly machine learning. However, when it
comes to graphs, large models have not achieved the same level of success as in
other fields, such as natural language processing and computer vision. In order
to promote applying large models for graphs forward, we present a perspective
paper to discuss the challenges and opportunities associated with developing
large graph models. First, we discuss the desired characteristics of large
graph models. Then, we present detailed discussions from three key
perspectives: representation basis, graph data, and graph models. In each
category, we provide a brief overview of recent advances and highlight the
remaining challenges together with our visions. Finally, we discuss valuable
applications of large graph models. We believe this perspective can encourage
further investigations into large graph models, ultimately pushing us one step
closer towards artificial general intelligence (AGI). We are the first to
comprehensively study large graph models, to the best of our knowledge.Comment: Accepted by NeurIPS 2023 New Frontiers in Graph Learning Workshop.
Comments are welcom
On Cognitive Preferences and the Plausibility of Rule-based Models
It is conventional wisdom in machine learning and data mining that logical
models such as rule sets are more interpretable than other models, and that
among such rule-based models, simpler models are more interpretable than more
complex ones. In this position paper, we question this latter assumption by
focusing on one particular aspect of interpretability, namely the plausibility
of models. Roughly speaking, we equate the plausibility of a model with the
likeliness that a user accepts it as an explanation for a prediction. In
particular, we argue that, all other things being equal, longer explanations
may be more convincing than shorter ones, and that the predominant bias for
shorter models, which is typically necessary for learning powerful
discriminative models, may not be suitable when it comes to user acceptance of
the learned models. To that end, we first recapitulate evidence for and against
this postulate, and then report the results of an evaluation in a
crowd-sourcing study based on about 3.000 judgments. The results do not reveal
a strong preference for simple rules, whereas we can observe a weak preference
for longer rules in some domains. We then relate these results to well-known
cognitive biases such as the conjunction fallacy, the representative heuristic,
or the recogition heuristic, and investigate their relation to rule length and
plausibility.Comment: V4: Another rewrite of section on interpretability to clarify focus
on plausibility and relation to interpretability, comprehensibility, and
justifiabilit
Adversarial Sampling and Training for Semi-Supervised Information Retrieval
Ad-hoc retrieval models with implicit feedback often have problems, e.g., the
imbalanced classes in the data set. Too few clicked documents may hurt
generalization ability of the models, whereas too many non-clicked documents
may harm effectiveness of the models and efficiency of training. In addition,
recent neural network-based models are vulnerable to adversarial examples due
to the linear nature in them. To solve the problems at the same time, we
propose an adversarial sampling and training framework to learn ad-hoc
retrieval models with implicit feedback. Our key idea is (i) to augment clicked
examples by adversarial training for better generalization and (ii) to obtain
very informational non-clicked examples by adversarial sampling and training.
Experiments are performed on benchmark data sets for common ad-hoc retrieval
tasks such as Web search, item recommendation, and question answering.
Experimental results indicate that the proposed approaches significantly
outperform strong baselines especially for high-ranked documents, and they
outperform IRGAN in NDCG@5 using only 5% of labeled data for the Web search
task.Comment: Published in WWW 201
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