mstyslavity/prisma_v3 huggingface dataset:
In this paper, we introduce a learning analytics framework to analyze the in-context learning (ICL) behavior of large language models (LLMs) through the lens of the Zone of Proximal Development (ZPD), an established theory in educational psychology. ZPD delineates the space between what a learner is capable of doing unsupported and what the learner cannot do even with support. We adapt this concept to ICL, measuring the ZPD of LLMs based on model performance on individual examples with and without ICL. Furthermore, we propose an item response theory (IRT) model to predict the distribution of zones for LLMs. Our findings reveal a series of intricate and multifaceted behaviors of ICL, providing new insights into understanding and leveraging this technique. Finally, we demonstrate how our framework can enhance LLM in both inference and fine-tuning scenarios: (1) By predicting a model's zone of proximal development, we selectively apply ICL to queries that are most likely to benefit from demonstrations, achieving a better balance between inference cost and performance; (2) We propose a human-like curriculum for fine-tuning, which prioritizes examples within the model's ZPD. The curriculum results in improved performance, and we explain its effectiveness through an analysis of the training dynamics of LLMs.
In this paper, an explainable deep learning-based classifier based on adaptive sinc filters for Parkinson's Disease diagnosis (PD) along with determining its severity, based on analyzing the gait cycle (SincPD) is presented. Considering the effects of PD on the gait cycle of patients, the proposed method utilizes raw data in the form of vertical Ground Reaction Force (vGRF) measured by wearable sensors placed in soles of subjects' shoes. The proposed method consists of Sinc layers that model adaptive bandpass filters to extract important frequency-bands in gait cycle of patients along with healthy subjects. Therefore, by considering these frequencies, the reasons behind the classification a person as a patient or healthy can be explained. In this method, after applying some preprocessing processes, a large model equipped with many filters is first trained. Next, to prune the extra units and reach a more explainable and parsimonious structure, the extracted filters are clusters based on their cut-off frequencies using a centroid-based clustering approach. Afterward, the medoids of the extracted clusters are considered as the final filters. Therefore, only 15 bandpass filters for each sensor are derived to classify patients and healthy subjects. Finally, the most effective filters along with the sensors are determined by comparing the energy of each filter encountering patients and healthy subjects.
Visual search is a fundamental natural task for humans and other animals. We investigated the decision processes humans use in covert (single-fixation) search with briefly presented displays having well-separated potential target locations. Performance was compared with the Bayesian-optimal decision process under the assumption that the information from the different potential target locations is statistically independent. Surprisingly, humans performed slightly better than optimal, despite humans' substantial loss of sensitivity in the fovea (foveal neglect), and the implausibility of the human brain replicating the optimal computations. We show that three factors can quantitatively explain these seemingly paradoxical results. Most importantly, simple and fixed heuristic decision rules reach near optimal search performance. Secondly, foveal neglect primarily affects only the central potential target location. Finally, spatially correlated neural noise can cause search performance to exceed that predicted for independent noise. These findings have broad implications for understanding visual search tasks and other identification tasks in humans and other animals.
This study explores strategies for efficiently classifying scientific full texts using both small, BERT-based models and local large language models like Llama-3.1 8B. We focus on developing methods for selecting subsets of input sentences to reduce input size while simultaneously enhancing classification performance. To this end, we compile a novel dataset consisting of full-text scientific papers from the field of invasion biology, specifically addressing the impacts of invasive species. These papers are aligned with publicly available impact assessments created by researchers for the International Union for Conservation of Nature (IUCN). Through extensive experimentation, we demonstrate that various sources like human evidence annotations, LLM-generated annotations or explainability scores can be used to train sentence selection models that improve the performance of both encoder- and decoder-based language models while optimizing efficiency through the reduction in input length, leading to improved results even if compared to models like ModernBERT that are able to handle the complete text as input. Additionally, we find that repeated sampling of shorter inputs proves to be a very effective strategy that, at a slightly increased cost, can further improve classification performance.
Context and Motivation: Due to their increasing complexity, everyday software systems are becoming increasingly opaque for users. A frequently adopted method to address this difficulty is explainability, which aims to make systems more understandable and usable. Question/problem: However, explanations can also lead to unnecessary cognitive load. Therefore, adapting explanations to the actual needs of a user is a frequently faced challenge. Principal ideas/results: This study investigates factors influencing users' preferred the level of detail and the form of an explanation (e.g., short text or video tutorial) in software. We conducted an online survey with 58 participants to explore relationships between demographics, software usage, app-specific knowledge, as well as their preferred explanation form and level of detail. The results indicate that users prefer moderately detailed explanations in short text formats. Correlation analyses revealed no relationship between app-specific knowledge and the preferred level of detail of an explanation, but an influence of demographic aspects (like gender) on app-specific knowledge and its impact on application confidence were observed, pointing to a possible mediated relationship between knowledge and preferences for explanations. Contribution: Our results show that explanation preferences are weakly influenced by app-specific knowledge but shaped by demographic and psychological factors, supporting the development of adaptive explanation systems tailored to user expertise. These findings support requirements analysis processes by highlighting important factors that should be considered in user-centered methods such as personas.
Context and Motivation: The increasing complexity of modern software systems often challenges users' abilities to interact with them. Taking established quality attributes such as usability and transparency into account can mitigate this problem, but often do not suffice to completely solve it. Recently, explainability has emerged as essential non-functional requirement to help overcome the aforementioned difficulties. Question/problem: User preferences regarding the integration of explanations in software differ. Neither too few nor too many explanations are helpful. In this paper, we investigate the influence of a user's subjective mood and objective demographic aspects on explanation needs by means of frequency and type of explanation. Principal ideas/results: Our results reveal a limited relationship between these factors and explanation needs. Two significant correlations were identified: Emotional reactivity was positively correlated with the need for UI explanations, while a negative correlation was found between age and user interface needs. Contribution: As we only find very few significant aspects that influence the need for explanations, we conclude that the need for explanations is very subjective and does only partially depend on objective factors. These findings emphasize the necessity for software companies to actively gather user-specific explainability requirements to address diverse and context-dependent user demands. Nevertheless, future research should explore additional personal traits and cross-cultural factors to inform the development of adaptive, user-centered explanation systems.
Trust models are essential components of networks of any nature, as they refer to confidence frameworks to evaluate and verify if their participants act reliably and fairly. They are necessary to any social, organizational, or computer network model to ensure truthful interactions, data integrity, and overall system resilience. Trust models can be centralized or distributed, each providing a good fair of benefits and challenges. Blockchain is a special case of distributed trust models that utilize advanced cryptographic techniques and decentralized consensus mechanisms to enforce confidence among participants within a network. In this piece, we provide an overview of blockchain networks from the trust model perspective, with a special focus on the Hyperledger Fabric framework, a widespread blockchain implementation with a consortium architecture. We explore Fabric in detail, including its trust model, components, overall architecture, and a general implementation blueprint for the platform. We intend to offer readers with technical backgrounds but not necessarily experts in the blockchain field a friendly review of these topics to spark their curiosity to continue expanding their knowledge on these increasingly popular technologies.
With an evolutionary approach, the basis of morality can be explained as adaptations to problems of cooperation. With 'evolution' taken in a broad sense, AIs that satisfy the conditions for evolution to apply will be subject to the same cooperative evolutionary pressure as biological entities. Here the adaptiveness of increased cooperation as material safety and wealth increase is discussed -- for humans, for other societies, and for AIs. Diminishing beneficial returns from increased access to material resources also suggests the possibility that, on the whole, there will be no incentive to for instance colonize entire galaxies, thus providing a possible explanation of the Fermi paradox, wondering where everybody is. It is further argued that old societies could engender, give way to, super-AIs, since it is likely that super-AIs are feasible, and fitter. Closing is an aside on effective ways for morals and goals to affect life and society, emphasizing environments, cultures, and laws, and exemplified by how to eat. 'Diminishing returns' is defined, as less than roots, the inverse of infeasibility. It is also noted that there can be no exponential colonization or reproduction, for mathematical reasons, as each entity takes up a certain amount of space. Appended are an algorithm for colonizing for example a galaxy quickly, models of the evolution of cooperation and fairness under diminishing returns, and software for simulating signaling development.
Numerous fairness metrics have been proposed and employed by artificial intelligence (AI) experts to quantitatively measure bias and define fairness in AI models. Recognizing the need to accommodate stakeholders' diverse fairness understandings, efforts are underway to solicit their input. However, conveying AI fairness metrics to stakeholders without AI expertise, capturing their personal preferences, and seeking a collective consensus remain challenging and underexplored. To bridge this gap, we propose a new framework, EARN Fairness, which facilitates collective metric decisions among stakeholders without requiring AI expertise. The framework features an adaptable interactive system and a stakeholder-centered EARN Fairness process to Explain fairness metrics, Ask stakeholders' personal metric preferences, Review metrics collectively, and Negotiate a consensus on metric selection. To gather empirical results, we applied the framework to a credit rating scenario and conducted a user study involving 18 decision subjects without AI knowledge. We identify their personal metric preferences and their acceptable level of unfairness in individual sessions. Subsequently, we uncovered how they reached metric consensus in team sessions. Our work shows that the EARN Fairness framework enables stakeholders to express personal preferences and reach consensus, providing practical guidance for implementing human-centered AI fairness in high-risk contexts. Through this approach, we aim to harmonize fairness expectations of diverse stakeholders, fostering more equitable and inclusive AI fairness.
The increasing complexity of software dependencies has led to the emergence of automated dependency management tools, such as Dependabot. However, these tools often overwhelm developers with a high volume of alerts and notifications, leading to alert fatigue. This paper presents a position on using Artificial Intelligence (AI) agents as dependency negotiators to reduce alert fatigue. We then examine specific use cases where AI agents can facilitate dependency negotiations, such as when working with external dependencies or managing complex, multi-component systems. Our findings highlight the need for more research on the design and evaluation of AI-driven dependency mediation mechanisms. With a focus on ensuring transparency, explainability, and human trustworthiness in these GitHub software projects, our goal is to reduce alert fatigue to an extent that maintainers no longer feel overwhelmed and welcome pull requests just like any other contribution into their projects.
We revisit the reference determinacy (RD) assumption in the task of natural language inference (NLI), i.e., the premise and hypothesis are assumed to refer to the same context when human raters annotate a label. While RD is a practical assumption for constructing a new NLI dataset, we observe that current NLI models, which are typically trained solely on hypothesis-premise pairs created with the RD assumption, fail in downstream applications such as fact verification, where the input premise and hypothesis may refer to different contexts. To highlight the impact of this phenomenon in real-world use cases, we introduce RefNLI, a diagnostic benchmark for identifying reference ambiguity in NLI examples. In RefNLI, the premise is retrieved from a knowledge source (i.e., Wikipedia) and does not necessarily refer to the same context as the hypothesis. With RefNLI, we demonstrate that finetuned NLI models and few-shot prompted LLMs both fail to recognize context mismatch, leading to over 80% false contradiction and over 50% entailment predictions. We discover that the existence of reference ambiguity in NLI examples can in part explain the inherent human disagreements in NLI and provide insight into how the RD assumption impacts the NLI dataset creation process.
Inductive reasoning - the process of inferring general rules from a small number of observations - is a fundamental aspect of human intelligence. Recent works suggest that large language models (LLMs) can engage in inductive reasoning by sampling multiple hypotheses about the rules and selecting the one that best explains the observations. However, due to the IID sampling, semantically redundant hypotheses are frequently generated, leading to significant wastage of compute. In this paper, we 1) demonstrate that increasing the temperature to enhance the diversity is limited due to text degeneration issue, and 2) propose a novel method to improve the diversity while maintaining text quality. We first analyze the effect of increasing the temperature parameter, which is regarded as the LLM's diversity control, on IID hypotheses. Our analysis shows that as temperature rises, diversity and accuracy of hypotheses increase up to a certain point, but this trend saturates due to text degeneration. To generate hypotheses that are more semantically diverse and of higher quality, we propose a novel approach inspired by human inductive reasoning, which we call Mixture of Concepts (MoC). When applied to several inductive reasoning benchmarks, MoC demonstrated significant performance improvements compared to standard IID sampling and other approaches.
Around 50 percent of Irelands rural population relies on unregulated private wells vulnerable to agricultural runoff and untreated wastewater. High national rates of Shiga toxin-producing Escherichia coli (STEC) and other waterborne illnesses have been linked to well water exposure. Periodic well testing is essential for public health, yet the lack of government incentives places the financial burden on households. Understanding environmental, cognitive, and material factors influencing well-testing behavior is critical. This study employs Agent-Based Modeling (ABM) to simulate policy interventions based on national survey data. The ABM framework, designed for private well-testing behavior, integrates a Deep Q-network reinforcement learning model and Explainable AI (XAI) for decision-making insights. Key features were selected using Recursive Feature Elimination (RFE) with 10-fold cross-validation, while SHAP (Shapley Additive Explanations) provided further interpretability for policy recommendations. Fourteen policy scenarios were tested. The most effective, Free Well Testing plus Communication Campaign, increased participation to 435 out of 561 agents, from a baseline of approximately 5 percent, with rapid behavioral adaptation. Free Well Testing plus Regulation also performed well, with 433 out of 561 agents initiating well testing. Free testing alone raised participation to over 75 percent, with some agents testing multiple times annually. Scenarios with free well testing achieved faster learning efficiency, converging in 1000 episodes, while others took 2000 episodes, indicating slower adaptation. This research demonstrates the value of ABM and XAI in public health policy, providing a framework for evaluating behavioral interventions in environmental health.
Deep learning-based expert models have reached superhuman performance in decision-making domains such as chess and Go. However, it is under-explored to explain or comment on given decisions although it is important for model explainability and human education. The outputs of expert models are accurate, but yet difficult to interpret for humans. On the other hand, large language models (LLMs) can produce fluent commentary but are prone to hallucinations due to their limited decision-making capabilities. To bridge this gap between expert models and LLMs, we focus on chess commentary as a representative task of explaining complex decision-making processes through language and address both the generation and evaluation of commentary. We introduce Concept-guided Chess Commentary generation (CCC) for producing commentary and GPT-based Chess Commentary Evaluation (GCC-Eval) for assessing it. CCC integrates the decision-making strengths of expert models with the linguistic fluency of LLMs through prioritized, concept-based explanations. GCC-Eval leverages expert knowledge to evaluate chess commentary based on informativeness and linguistic quality. Experimental results, validated by both human judges and GCC-Eval, demonstrate that CCC generates commentary which is accurate, informative, and fluent.
Deep Reinforcement Learning (DRL) has achieved remarkable success in sequential decision-making tasks across diverse domains, yet its reliance on black-box neural architectures hinders interpretability, trust, and deployment in high-stakes applications. Explainable Deep Reinforcement Learning (XRL) addresses these challenges by enhancing transparency through feature-level, state-level, dataset-level, and model-level explanation techniques. This survey provides a comprehensive review of XRL methods, evaluates their qualitative and quantitative assessment frameworks, and explores their role in policy refinement, adversarial robustness, and security. Additionally, we examine the integration of reinforcement learning with Large Language Models (LLMs), particularly through Reinforcement Learning from Human Feedback (RLHF), which optimizes AI alignment with human preferences. We conclude by highlighting open research challenges and future directions to advance the development of interpretable, reliable, and accountable DRL systems.
Background and Context: Some skills taught in introductory programming courses are categorized into 1) explaining code, 2) arranging lines of code in correct sequence, 3) tracing through the execution of a program, and 4) writing code from scratch. Objective: Knowing if a programming skill is a prerequisite to another would benefit teachers in properly planning the course and structuring the order in which they present activities relating to new content. Prior attempts to establish a skill hierarchy have suffered from methodological issues. Method: In this study, we used the conviction measure from association rule mining to perform pair-wise comparisons of five skills: Write, Trace, Reverse trace, Sequence, and Explain code. We used the data from four exams with more than 600 participants where students solved programming assignments of different skills for several programming topics. Findings: Our findings matched the previous finding that tracing is a prerequisite for students to learn to write code. Contradicting the previous claims, our analysis showed that using the mean threshold writing code is a prerequisite to explaining code. However, there is no clear relationship when we change the threshold to the median. Unlike prior work, we did not find a clear prerequisite relationship between sequencing code and writing or explaining code. Implications: Our research can help instructors by systematically arranging the skills students exercise when encountering a new topic. The goal is to help instructors properly teach and assess programming in a fashion most effective for learning by leveraging the relationship between skills.
Free-text explanations are expressive and easy to understand, but many datasets lack annotated explanation data, making it challenging to train models for explainable predictions. To address this, we investigate how to use existing explanation datasets for self-rationalization and evaluate models' out-of-distribution (OOD) performance. We fine-tune T5-Large and OLMo-7B models and assess the impact of fine-tuning data quality, the number of fine-tuning samples, and few-shot selection methods. The models are evaluated on 19 diverse OOD datasets across three tasks: natural language inference (NLI), fact-checking, and hallucination detection in abstractive summarization. For the generated explanation evaluation, we conduct a human study on 13 selected models and study its correlation with the Acceptability score (T5-11B) and three other LLM-based reference-free metrics. Human evaluation shows that the Acceptability score correlates most strongly with human judgments, demonstrating its effectiveness in evaluating free-text explanations. Our findings reveal: 1) few annotated examples effectively adapt models for OOD explanation generation; 2) compared to sample selection strategies, fine-tuning data source has a larger impact on OOD performance; and 3) models with higher label prediction accuracy tend to produce better explanations, as reflected by higher Acceptability scores.