Given a graph with textual attributes, we enable users to `chat with their
graph': that is, to ask questions about the graph using a conversational
interface. In response to a user's questions, our method provides textual
replies and highlights the relevant parts of the graph. While existing works
integrate large language models (LLMs) and graph neural networks (GNNs) in
various ways, they mostly focus on either conventional graph tasks (such as
node, edge, and graph classification), or on answering simple graph queries on
small or synthetic graphs. In contrast, we develop a flexible
question-answering framework targeting real-world textual graphs, applicable to
multiple applications including scene graph understanding, common sense
reasoning, and knowledge graph reasoning. Toward this goal, we first develop
our Graph Question Answering (GraphQA) benchmark with data collected from
different tasks. Then, we propose our G-Retriever approach, which integrates
the strengths of GNNs, LLMs, and Retrieval-Augmented Generation (RAG), and can
be fine-tuned to enhance graph understanding via soft prompting. To resist
hallucination and to allow for textual graphs that greatly exceed the LLM's
context window size, G-Retriever performs RAG over a graph by formulating this
task as a Prize-Collecting Steiner Tree optimization problem. Empirical
evaluations show that our method outperforms baselines on textual graph tasks
from multiple domains, scales well with larger graph sizes, and resists
hallucination. (Our codes and datasets are available at:
this https URL)

给定具有文本属性的图，我们使用对话界面使用户能够与其图进行交流，在回答用户问题时提供文本回复并突出显示图的相关部分。通过开发我们的图问题回答（GraphQA）基准和集成 GNN、LLM 和 RAG 的 G-Retriever 方法，我们在多个领域的文本图任务中超越基准，而且可以适应更大的图大小并抗幻觉。

G-Retriever：文本图理解与问答的检索增强生成

G-Retriever: Retrieval-Augmented Generation for Textual Graph  Understanding and Question Answering

While recently developed NLP explainability methods let us open the black box
in various ways (Madsen et al., 2022), a missing ingredient in this endeavor is
an interactive tool offering a conversational interface. Such a dialogue system
can help users explore datasets and models with explanations in a
contextualized manner, e.g. via clarification or follow-up questions, and
through a natural language interface. We adapt the conversational explanation
framework TalkToModel (Slack et al., 2022) to the NLP domain, add new
NLP-specific operations such as free-text rationalization, and illustrate its
generalizability on three NLP tasks (dialogue act classification, question
answering, hate speech detection). To recognize user queries for explanations,
we evaluate fine-tuned and few-shot prompting models and implement a novel
Adapter-based approach. We then conduct two user studies on (1) the perceived
correctness and helpfulness of the dialogues, and (2) the simulatability, i.e.
how objectively helpful dialogical explanations are for humans in figuring out
the model's predicted label when it's not shown. We found rationalization and
feature attribution were helpful in explaining the model behavior. Moreover,
users could more reliably predict the model outcome based on an explanation
dialogue rather than one-off explanations.

通过在 NLP 领域中使用具有自由文本理性化功能的对话系统，研究人员对 NLP 任务进行了改进，并通过评估模型性能和用户研究，证明了理性化和特征归因在解释模型行为方面的有效性。

通过对话式解释探索 NLP 模型和数据集的 InterroLang

InterroLang: Exploring NLP Models and Datasets through Dialogue-based  Explanations

Tabular data is the most common format to publish and exchange structured
data online. A clear example is the growing number of open data portals
published by all types of public administrations. However, exploitation of
these data sources is currently limited to technical people able to
programmatically manipulate and digest such data. As an alternative, we propose
the use of chatbots to offer a conversational interface to facilitate the
exploration of tabular data sources. With our approach, any regular citizen can
benefit and leverage them. Moreover, our chatbots are not manually created:
instead, they are automatically generated from the data source itself thanks to
the instantiation of a configurable collection of conversation patterns.

采用聊天机器人来提供对表格数据源的交互式探索，可以使公民利用数据。聊天机器人是通过一种可配置的对话模式从数据源自动生成的。

自动生成面向表格数据探索的对话界面

Towards the Automatic Generation of Conversational Interfaces to  Facilitate the Exploration of Tabular Data

While various AI explanation (XAI) methods have been proposed to interpret AI
systems, whether the state-of-the-art XAI methods are practically useful for
humans remains inconsistent findings. To improve the usefulness of XAI methods,
a line of studies identifies the gaps between the diverse and dynamic
real-world user needs with the status quo of XAI methods. Although prior
studies envision mitigating these gaps by integrating multiple XAI methods into
the universal XAI interfaces (e.g., conversational or GUI-based XAI systems),
there is a lack of work investigating how these systems should be designed to
meet practical user needs. In this study, we present ConvXAI, a conversational
XAI system that incorporates multiple XAI types, and empowers users to request
a variety of XAI questions via a universal XAI dialogue interface.
Particularly, we innovatively embed practical user needs (i.e., four principles
grounding on the formative study) into ConvXAI design to improve practical
usefulness. Further, we design the domain-specific language (DSL) to implement
the essential conversational XAI modules and release the core conversational
universal XAI API for generalization. The findings from two within-subjects
studies with 21 users show that ConvXAI is more useful for humans in perceiving
the understanding and writing improvement, and improving the writing process in
terms of productivity and sentence quality. Finally, this work contributes
insight into the design space of useful XAI, reveals humans' XAI usage patterns
with empirical evidence in practice, and identifies opportunities for future
useful XAI work.

通过实验展示了 ConvXAI, 一个将多种 XAI 类型整合到一个通用对话接口上，利用实际用户需求来提高实用性，在人们感知、写作和生产力方面提高了实用性。