195,825 research outputs found
This Looks Like That, Because ... Explaining Prototypes for Interpretable Image Recognition
Image recognition with prototypes is considered an interpretable alternative
for black box deep learning models. Classification depends on the extent to
which a test image "looks like" a prototype. However, perceptual similarity for
humans can be different from the similarity learned by the classification
model. Hence, only visualising prototypes can be insufficient for a user to
understand what a prototype exactly represents, and why the model considers a
prototype and an image to be similar. We address this ambiguity and argue that
prototypes should be explained. We improve interpretability by automatically
enhancing visual prototypes with textual quantitative information about visual
characteristics deemed important by the classification model. Specifically, our
method clarifies the meaning of a prototype by quantifying the influence of
colour hue, shape, texture, contrast and saturation and can generate both
global and local explanations. Because of the generality of our approach, it
can improve the interpretability of any similarity-based method for
prototypical image recognition. In our experiments, we apply our method to the
existing Prototypical Part Network (ProtoPNet). Our analysis confirms that the
global explanations are generalisable, and often correspond to the visually
perceptible properties of a prototype. Our explanations are especially relevant
for prototypes which might have been interpreted incorrectly otherwise. By
explaining such 'misleading' prototypes, we improve the interpretability and
simulatability of a prototype-based classification model. We also use our
method to check whether visually similar prototypes have similar explanations,
and are able to discover redundancy. Code is available at
https://github.com/M-Nauta/Explaining_Prototypes .Comment: 10 pages, 9 figure
Automated Assessment of Students' Code Comprehension using LLMs
Assessing student's answers and in particular natural language answers is a
crucial challenge in the field of education. Advances in machine learning,
including transformer-based models such as Large Language Models(LLMs), have
led to significant progress in various natural language tasks. Nevertheless,
amidst the growing trend of evaluating LLMs across diverse tasks, evaluating
LLMs in the realm of automated answer assesment has not received much
attention. To address this gap, we explore the potential of using LLMs for
automated assessment of student's short and open-ended answer. Particularly, we
use LLMs to compare students' explanations with expert explanations in the
context of line-by-line explanations of computer programs.
For comparison purposes, we assess both Large Language Models (LLMs) and
encoder-based Semantic Textual Similarity (STS) models in the context of
assessing the correctness of students' explanation of computer code. Our
findings indicate that LLMs, when prompted in few-shot and chain-of-thought
setting perform comparable to fine-tuned encoder-based models in evaluating
students' short answers in programming domain
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Finding New Rules for Incomplete Theories: Induction with Explicit Biases in Varying Contexts
Many AI problem solvers possess explicitly encoded knowledge - a domain theory ““ that they use to solve problems. If these problem solvers are to be autonomous, they must be able to detect and to fill gaps in their own knowledge. The field of machine learning addresses this issue. Recently two disparate machine learning approaches have emerged as predominant in the field: explanation-based learning (EBL) and similarity-based learning (SBL), EBL and SBL have been applied to problems in a variety of domains. Both methods have clear problems, however, EBL assumes that a system is given an explicit theory of the domain that is complete, correct, and tractable. These assumptions are clearly unrealistic for most complex, real-world problems. SBL suffers because of its lack of an explicit theory of the domain. The simplicity of the method requires that human intervention playa large role in tailoring input examples and the features describing them in such a way as to allow a system to choose an appropriate set of features to define a concept. Biasing a system in this way may result in its being unable to discover all concepts in even a Single domain. Less tailoring of the examples leaves a system open to the possibility of not converging on the best definition for a concept, or any at all, due to the computational complexity. The research described in this proposal addresses a number of the problems found in explanation-based and similarity-based learning. The major focus of the research is the elimination of the assumption that the domain theory of an EBL system is complete. In particular, it considers the problem of working with an incomplete theory by suggesting a method by which gaps in an EBL system's knowledge can be detected and filled. We suggest that when EBL cannot derive a complete explanation, the partial explanation focus a context in which learning takes place. Information extracted from partial explanations, as well as from complete explanations, can be exploited by SBL to do better induction of the missing domain knowledge. The extracted information constitutes an explicit bias for similarity-based learning. A second problem to be addressed is that of making the biases of SBL explicit. Finally, all testing of the claims made in this proposal is to be done in the Gemini learning system. The development of the system addresses the goal of constructing an integrated learning architecture utilizing both EBL and SBL
Investigating the quality of mental models deployed by undergraduate engineering students in creating explanations: The case of thermally activated phenomena
This paper describes a method aimed at pointing out the quality of the mental models undergraduate engineering students deploy when asked to create explanations for phenomena or processes and/or use a given model in the same context. Student responses to a specially designed written questionnaire are quantitatively analyzed using researcher-generated categories of reasoning, based on the physics education research literature on student understanding of the relevant physics content. The use of statistical implicative analysis tools allows us to successfully identify clusters of students with respect to the similarity to the reasoning categories, defined as "practical or everyday," "descriptive," or "explicative." Through the use of similarity and implication indexes our method also enables us to study the consistency in students' deployment of mental models. A qualitative analysis of interviews conducted with students after they had completed the questionnaire is used to clarify some aspects which emerged from the quantitative analysis and validate the results obtained. Some implications of this joint use of quantitative and qualitative analysis for the design of a learning environment focused on the understanding of some aspects of the world at the level of causation and mechanisms of functioning are discussed
Contrastive Corpus Attribution for Explaining Representations
Despite the widespread use of unsupervised models, very few methods are
designed to explain them. Most explanation methods explain a scalar model
output. However, unsupervised models output representation vectors, the
elements of which are not good candidates to explain because they lack semantic
meaning. To bridge this gap, recent works defined a scalar explanation output:
a dot product-based similarity in the representation space to the sample being
explained (i.e., an explicand). Although this enabled explanations of
unsupervised models, the interpretation of this approach can still be opaque
because similarity to the explicand's representation may not be meaningful to
humans. To address this, we propose contrastive corpus similarity, a novel and
semantically meaningful scalar explanation output based on a reference corpus
and a contrasting foil set of samples. We demonstrate that contrastive corpus
similarity is compatible with many post-hoc feature attribution methods to
generate COntrastive COrpus Attributions (COCOA) and quantitatively verify that
features important to the corpus are identified. We showcase the utility of
COCOA in two ways: (i) we draw insights by explaining augmentations of the same
image in a contrastive learning setting (SimCLR); and (ii) we perform zero-shot
object localization by explaining the similarity of image representations to
jointly learned text representations (CLIP).Comment: Updated for the final camera-ready version of ICLR 202
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