168 research outputs found
ConceptExplainer: Understanding the Mental Model of Deep Learning Algorithms via Interactive Concept-based Explanations
Traditional deep learning interpretability methods which are suitable for
non-expert users cannot explain network behaviors at the global level and are
inflexible at providing fine-grained explanations. As a solution, concept-based
explanations are gaining attention due to their human intuitiveness and their
flexibility to describe both global and local model behaviors. Concepts are
groups of similarly meaningful pixels that express a notion, embedded within
the network's latent space and have primarily been hand-generated, but have
recently been discovered by automated approaches. Unfortunately, the magnitude
and diversity of discovered concepts makes it difficult for non-experts to
navigate and make sense of the concept space, and lack of easy-to-use software
also makes concept explanations inaccessible to many non-expert users. Visual
analytics can serve a valuable role in bridging these gaps by enabling
structured navigation and exploration of the concept space to provide
concept-based insights of model behavior to users. To this end, we design,
develop, and validate ConceptExplainer, a visual analytics system that enables
non-expert users to interactively probe and explore the concept space to
explain model behavior at the instance/class/global level. The system was
developed via iterative prototyping to address a number of design challenges
that non-experts face in interpreting the behavior of deep learning models. Via
a rigorous user study, we validate how ConceptExplainer supports these
challenges. Likewise, we conduct a series of usage scenarios to demonstrate how
the system supports the interactive analysis of model behavior across a variety
of tasks and explanation granularities, such as identifying concepts that are
important to classification, identifying bias in training data, and
understanding how concepts can be shared across diverse and seemingly
dissimilar classes.Comment: 9 pages, 6 figure
People's Perceptions Toward Bias and Related Concepts in Large Language Models: A Systematic Review
Large language models (LLMs) have brought breakthroughs in tasks including
translation, summarization, information retrieval, and language generation,
gaining growing interest in the CHI community. Meanwhile, the literature shows
researchers' controversial perceptions about the efficacy, ethics, and
intellectual abilities of LLMs. However, we do not know how lay people perceive
LLMs that are pervasive in everyday tools, specifically regarding their
experience with LLMs around bias, stereotypes, social norms, or safety. In this
study, we conducted a systematic review to understand what empirical insights
papers have gathered about people's perceptions toward LLMs. From a total of
231 retrieved papers, we full-text reviewed 15 papers that recruited human
evaluators to assess their experiences with LLMs. We report different biases
and related concepts investigated by these studies, four broader LLM
application areas, the evaluators' perceptions toward LLMs' performances
including advantages, biases, and conflicting perceptions, factors influencing
these perceptions, and concerns about LLM applications
DPVis: Visual Analytics with Hidden Markov Models for Disease Progression Pathways
Clinical researchers use disease progression models to understand patient
status and characterize progression patterns from longitudinal health records.
One approach for disease progression modeling is to describe patient status
using a small number of states that represent distinctive distributions over a
set of observed measures. Hidden Markov models (HMMs) and its variants are a
class of models that both discover these states and make inferences of health
states for patients. Despite the advantages of using the algorithms for
discovering interesting patterns, it still remains challenging for medical
experts to interpret model outputs, understand complex modeling parameters, and
clinically make sense of the patterns. To tackle these problems, we conducted a
design study with clinical scientists, statisticians, and visualization
experts, with the goal to investigate disease progression pathways of chronic
diseases, namely type 1 diabetes (T1D), Huntington's disease, Parkinson's
disease, and chronic obstructive pulmonary disease (COPD). As a result, we
introduce DPVis which seamlessly integrates model parameters and outcomes of
HMMs into interpretable and interactive visualizations. In this study, we
demonstrate that DPVis is successful in evaluating disease progression models,
visually summarizing disease states, interactively exploring disease
progression patterns, and building, analyzing, and comparing clinically
relevant patient subgroups.Comment: to appear at IEEE Transactions on Visualization and Computer Graphic
PromptAid: Prompt Exploration, Perturbation, Testing and Iteration using Visual Analytics for Large Language Models
Large Language Models (LLMs) have gained widespread popularity due to their
ability to perform ad-hoc Natural Language Processing (NLP) tasks with a simple
natural language prompt. Part of the appeal for LLMs is their approachability
to the general public, including individuals with no prior technical experience
in NLP techniques. However, natural language prompts can vary significantly in
terms of their linguistic structure, context, and other semantics. Modifying
one or more of these aspects can result in significant differences in task
performance. Non-expert users may find it challenging to identify the changes
needed to improve a prompt, especially when they lack domain-specific knowledge
and lack appropriate feedback. To address this challenge, we present PromptAid,
a visual analytics system designed to interactively create, refine, and test
prompts through exploration, perturbation, testing, and iteration. PromptAid
uses multiple, coordinated visualizations which allow users to improve prompts
by using the three strategies: keyword perturbations, paraphrasing
perturbations, and obtaining the best set of in-context few-shot examples.
PromptAid was designed through an iterative prototyping process involving NLP
experts and was evaluated through quantitative and qualitative assessments for
LLMs. Our findings indicate that PromptAid helps users to iterate over prompt
template alterations with less cognitive overhead, generate diverse prompts
with help of recommendations, and analyze the performance of the generated
prompts while surpassing existing state-of-the-art prompting interfaces in
performance
Spherical similarity explorer for comparative case analysis
Comparative Case Analysis (CCA) is an important tool for criminal investigation and crime theory extraction. It analyzes the commonalities and differences between a collection of crime reports in order to understand crime patterns and identify abnormal cases. A big challenge of CCA is the data processing and exploration. Traditional manual approach can no longer cope with the increasing volume and complexity of the data. In this paper we introduce a novel visual analytics system, Spherical Similarity Explorer (SSE) that automates the data processing process and provides interactive visualizations to support the data exploration. We illustrate the use of the system with uses cases that involve real world application data and evaluate the system with criminal intelligence analysts
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