心理治疗干预技术是治疗师和患者之间的多方面对话。与一般的临床讨论不同，心理治疗的核心成分（即症状）很难区分，因此成为以后要总结的复杂问题。结构化的咨询对话可能包含有关症状，心理健康问题病史或发现患者行为的讨论。它还可能包含与临床摘要无关的讨论填充单词。我们将结构化心理治疗的这些要素称为咨询组成部分。在本文中，目的是心理健康咨询的摘要，以基于领域知识并帮助临床医生快速收集意义。在注释咨询组件的12.9k话语和每次对话的参考摘要之后，我们创建了一个新的数据集。此外，我们建议消费是一种新颖的咨询组件指导摘要模型。消费经历三个独立模块。首先，为了评估抑郁症状的存在，它使用患者健康问卷（PHQ-9）过滤了话语，而第二和第三模块旨在对咨询组件进行分类。最后，我们提出了针对特定问题的心理健康信息捕获（MHIC）评估指标，用于咨询摘要。我们的比较研究表明，我们改善了性能并产生凝聚力，语义和连贯的摘要。我们全面分析了生成的摘要，以研究心理治疗元素的捕获。摘要的人类和临床评估表明，消费会产生质量摘要。此外，心理健康专家验证了消费的临床可接受性。最后，我们讨论了现实世界中心理健康咨询摘要的独特性，并在Mathic.ai的支持下显示了其在线应用程序上的部署的证据

Covid-19大流行的发作使风险的心理健康带来了。社会咨询在这种环境中取得了显着意义。与一般面向目标的对话不同，患者和治疗师之间的对话是相当明暗的，尽管谈话的目标非常明显。在这种情况下，了解患者的目的在提供治疗会话中提供有效咨询方面是必要的，同样适用于对话系统。在这项工作中，我们前进是一个小小的一步，在开发精神健康咨询的自动对话系统中。我们开发一个名为HOPE的新型数据集，为咨询谈话中的对话行为分类提供平台。我们确定此类对话的要求，并提出了12个域特定的对话法（DAC）标签。我们收集12.9k的话语从youtube上公开的咨询会话视频，用DAC标签提取他们的成绩单，清洁并注释它们。此外，我们提出了一种基于变压器的架构的Sparta，具有新颖的扬声器和时间感知的语境学习，用于对话行动分类。我们的评价显示了若干基线的令人信服的表现，实现了最先进的希望。我们还通过对Sparta进行广泛的实证和定性分析来补充我们的实验。

Psychology research has long explored aspects of human personality such as extroversion, agreeableness and emotional stability. Categorizations like the `Big Five' personality traits are commonly used to assess and diagnose personality types. In this work, we explore the question of whether the perceived personality in language models is exhibited consistently in their language generation. For example, is a language model such as GPT2 likely to respond in a consistent way if asked to go out to a party? We also investigate whether such personality traits can be controlled. We show that when provided different types of contexts (such as personality descriptions, or answers to diagnostic questions about personality traits), language models such as BERT and GPT2 can consistently identify and reflect personality markers in those contexts. This behavior illustrates an ability to be manipulated in a highly predictable way, and frames them as tools for identifying personality traits and controlling personas in applications such as dialog systems. We also contribute a crowd-sourced data-set of personality descriptions of human subjects paired with their `Big Five' personality assessment data, and a data-set of personality descriptions collated from Reddit.

Many real-world applications of language models (LMs), such as code autocomplete and writing assistance, involve human-LM interaction, but the main LM benchmarks are non-interactive, where a system produces output without human intervention. To evaluate human-LM interaction, we develop a framework, Human-AI Language-based Interaction Evaluation (H-LINE), that expands non-interactive evaluation along three dimensions, capturing (i) the interactive process, not only the final output; (ii) the first-person subjective experience, not just a third-party assessment; and (iii) notions of preference beyond quality. We then design five tasks ranging from goal-oriented to open-ended to capture different forms of interaction. On four state-of-the-art LMs (three variants of OpenAI's GPT-3 and AI21's J1-Jumbo), we find that non-interactive performance does not always result in better human-LM interaction and that first-person and third-party metrics can diverge, suggesting the importance of examining the nuances of human-LM interaction.

Bike sharing systems often suffer from poor capacity management as a result of variable demand. These bike sharing systems would benefit from models to predict demand in order to moderate the number of bikes stored at each station. In this paper, we attempt to apply a graph neural network model to predict bike demand in the New York City, Citi Bike dataset.

A hallmark of human intelligence is the ability to learn new concepts purely from language. Several recent approaches have explored training machine learning models via natural language supervision. However, these approaches fall short in leveraging linguistic quantifiers (such as 'always' or 'rarely') and mimicking humans in compositionally learning complex tasks. Here, we present LaSQuE, a method that can learn zero-shot classifiers from language explanations by using three new strategies - (1) modeling the semantics of linguistic quantifiers in explanations (including exploiting ordinal strength relationships, such as 'always' > 'likely'), (2) aggregating information from multiple explanations using an attention-based mechanism, and (3) model training via curriculum learning. With these strategies, LaSQuE outperforms prior work, showing an absolute gain of up to 7% in generalizing to unseen real-world classification tasks.

Large Language Models (LLMs) have been the subject of active research, significantly advancing the field of Natural Language Processing (NLP). From BERT to BLOOM, LLMs have surpassed state-of-the-art results in various natural language tasks such as question answering, summarization, and text generation. Many ongoing efforts focus on understanding LLMs' capabilities, including their knowledge of the world, syntax, and semantics. However, extending the textual prowess of LLMs to symbolic reasoning has been slow and predominantly focused on tackling problems related to the mathematical field. In this paper, we explore the use of LLMs for automated planning - a branch of AI concerned with the realization of action sequences (plans) to achieve a goal, typically executed by intelligent agents, autonomous robots, and unmanned vehicles. We introduce Plansformer; an LLM fine-tuned on planning problems and capable of generating plans with favorable behavior in terms of correctness and length with reduced knowledge-engineering efforts. We also demonstrate the adaptability of Plansformer in solving different planning domains with varying complexities, owing to the transfer learning abilities of LLMs. For one configuration of Plansformer, we achieve ~97% valid plans, out of which ~95% are optimal for Towers of Hanoi - a puzzle-solving domain.

Chatbots, or bots for short, are multi-modal collaborative assistants that can help people complete useful tasks. Usually, when chatbots are referenced in connection with elections, they often draw negative reactions due to the fear of mis-information and hacking. Instead, in this paper, we explore how chatbots may be used to promote voter participation in vulnerable segments of society like senior citizens and first-time voters. In particular, we build a system that amplifies official information while personalizing it to users' unique needs transparently. We discuss its design, build prototypes with frequently asked questions (FAQ) election information for two US states that are low on an ease-of-voting scale, and report on its initial evaluation in a focus group. Our approach can be a win-win for voters, election agencies trying to fulfill their mandate and democracy at large.

This paper presents a new approach for analyzing and identifying potentially useful generalized plans. It presents a new conceptual framework along with an algorithmic process for assessing termination and reachability related properties of generalized plans. The presented framework builds upon classic results on the analysis of graphs to decompose generalized plans into smaller components in a novel algorithm for conducting a hierarchical analysis for termination of arbitrary generalized plans. Theoretical analysis of the new framework establishes soundness of the presented algorithms and shows how it goes beyond existing approaches; empirical analysis illustrates the scope of this approach. Our analysis shows that this new approach can effectively identify termination for a significantly larger class of generalized plans than was possible using existing methods.

The primary obstacle to developing technologies for low-resource languages is the lack of representative, usable data. In this paper, we report the deployment of technology-driven data collection methods for creating a corpus of more than 60,000 translations from Hindi to Gondi, a low-resource vulnerable language spoken by around 2.3 million tribal people in south and central India. During this process, we help expand information access in Gondi across 2 different dimensions (a) The creation of linguistic resources that can be used by the community, such as a dictionary, children's stories, Gondi translations from multiple sources and an Interactive Voice Response (IVR) based mass awareness platform; (b) Enabling its use in the digital domain by developing a Hindi-Gondi machine translation model, which is compressed by nearly 4 times to enable it's edge deployment on low-resource edge devices and in areas of little to no internet connectivity. We also present preliminary evaluations of utilizing the developed machine translation model to provide assistance to volunteers who are involved in collecting more data for the target language. Through these interventions, we not only created a refined and evaluated corpus of 26,240 Hindi-Gondi translations that was used for building the translation model but also engaged nearly 850 community members who can help take Gondi onto the internet.