Acquiring and Using Limited User Models in NLG

It is a truism of NLG that good knowledge of the reader can improve the quality of generated texts, and many NLG systems have been developed that exploit detailed user models when generating texts. Unfortunately, it is very difficult in practice to obtain detailed information about users. In this paper we describe our experiences in acquiring and using limited user models for NLG in four different systems, each of which took a different approach to this issue. One general conclusion is that it is useful if imperfect user models are understandable to users or domain experts, and indeed perhaps can be directly edited by them; this agrees with recent thinking about user models in other applications such as intelligent tutoring systems (Kay, 2001)

Integrating Human Expert Knowledge with OpenAI and ChatGPT: A Secure and Privacy-Enabled Knowledge Acquisition Approach

Advanced Large Language Models (LLMs) struggle to produce accurate results and preserve user privacy for use cases involving domain-specific knowledge. A privacy-preserving approach for leveraging LLM capabilities on domain-specific knowledge could greatly expand the use cases of LLMs in a variety of disciplines and industries. This project explores a method for acquiring domain-specific knowledge for use with GPT3 while protecting sensitive user information with ML-based text-sanitization

The Need for User Models in Generating Expert System Explanations

An explanation facility is an important component of an expert system, but current systems for the most part have neglected the importance of tailoring a system\u27s explanations to the user. This paper explores the role of user modeling in generating expert system explanations, making the claim that individualized user models are essential to produce good explanations when the system users vary in their knowledge of the domain, or in their goals, plans, and preferences. To make this argument, a characterization of explanation, and good explanation is made, leading to a presentation of how knowledge about the user affects the various aspects of a good explanation. Individualized user models are not only important, it is practical to obtain them. A method for acquiring a model of the user\u27s beliefs implicitly by eavesdropping on the interaction between user and system is presented, along with examples of how this information can be used to tailor an explanation

Implicit Acquisition of User Models in Cooperative Advisory Systems

User modelling systems to date have relied heavily on user models that were hand crafted for use in a particular situation. Recently, attention has focused on the feasibility of general user models, models that can be transferred from one situation to another with little or no modification. Such a general user model could be implemented as a modular component easily integrated into diverse systems. This paper addresses one class of general user models, those general with respect to the underlying domain of the application. In particular, a domain independent user modelling module for cooperative advisory systems is discussed. A major problem in building user models is the difficulty of acquiring information about the user. Traditional approaches have relied heavily on information that is pre-encoded by the system designer. For a user model to be domain independent, acquisition of knowledge will have to be done implicitly, i.e., knowledge about the user must be acquired during his interaction with the system. The research proposed in this paper focuses on domain independent implicit user model acquisition techniques for cooperative advisory systems. These techniques have been formalized as a set of model acquisition rules that will serve as the basis for the implementation of the model acquisition portion of a general user modelling module. The acquisition rules have been developed by studying a large number of conversations between advice-seekers and an expert. The rules presented are capable of supporting most of the modelling requirements of the expert in these conversations. Future work includes implementing these acquisition rules in a general user modelling module to test their effectiveness and domain independence

Estimating the effect of healthcare-associated infections on excess length of hospital stay using inverse probability-weighted survival curves

Background: Studies estimating excess length of stay (LOS) attributable to nosocomial infections have failed to address time-varying confounding, likely leading to overestimation of their impact. We present a methodology based on inverse probability–weighted survival curves to address this limitation. Methods: A case study focusing on intensive care unit–acquired bacteremia using data from 2 general intensive care units (ICUs) from 2 London teaching hospitals were used to illustrate the methodology. The area under the curve of a conventional Kaplan-Meier curve applied to the observed data was compared with that of an inverse probability–weighted Kaplan-Meier curve applied after treating bacteremia as censoring events. Weights were based on the daily probability of acquiring bacteremia. The difference between the observed average LOS and the average LOS that would be observed if all bacteremia cases could be prevented was multiplied by the number of admitted patients to obtain the total excess LOS. Results: The estimated total number of extra ICU days caused by 666 bacteremia cases was estimated at 2453 (95% confidence interval [CI], 1803–3103) days. The excess number of days was overestimated when ignoring time-varying confounding (2845 [95% CI, 2276–3415]) or when completely ignoring confounding (2838 [95% CI, 2101–3575]). Conclusions: ICU-acquired bacteremia was associated with a substantial excess LOS. Wider adoption of inverse probability–weighted survival curves or alternative techniques that address time-varying confounding could lead to better informed decision making around nosocomial infections and other time-dependent exposures

Ontology-based specific and exhaustive user profiles for constraint information fusion for multi-agents

Intelligent agents are an advanced technology utilized in Web Intelligence. When searching information from a distributed Web environment, information is retrieved by multi-agents on the client site and fused on the broker site. The current information fusion techniques rely on cooperation of agents to provide statistics. Such techniques are computationally expensive and unrealistic in the real world. In this paper, we introduce a model that uses a world ontology constructed from the Dewey Decimal Classification to acquire user profiles. By search using specific and exhaustive user profiles, information fusion techniques no longer rely on the statistics provided by agents. The model has been successfully evaluated using the large INEX data set simulating the distributed Web environment

Acquiring Correct Knowledge for Natural Language Generation

Natural language generation (NLG) systems are computer software systems that produce texts in English and other human languages, often from non-linguistic input data. NLG systems, like most AI systems, need substantial amounts of knowledge. However, our experience in two NLG projects suggests that it is difficult to acquire correct knowledge for NLG systems; indeed, every knowledge acquisition (KA) technique we tried had significant problems. In general terms, these problems were due to the complexity, novelty, and poorly understood nature of the tasks our systems attempted, and were worsened by the fact that people write so differently. This meant in particular that corpus-based KA approaches suffered because it was impossible to assemble a sizable corpus of high-quality consistent manually written texts in our domains; and structured expert-oriented KA techniques suffered because experts disagreed and because we could not get enough information about special and unusual cases to build robust systems. We believe that such problems are likely to affect many other NLG systems as well. In the long term, we hope that new KA techniques may emerge to help NLG system builders. In the shorter term, we believe that understanding how individual KA techniques can fail, and using a mixture of different KA techniques with different strengths and weaknesses, can help developers acquire NLG knowledge that is mostly correct