A Methodology for Gradual Semantics for Structured Argumentation under Incomplete Information

Gradual semantics have demonstrated great potential in argumentation, in particular for deploying quantitative bipolar argumentation frameworks (QBAFs) in a number of real-world settings, from judgmental forecasting to explainable AI. In this paper, we provide a novel methodology for obtaining gradual semantics for structured argumentation frameworks, where the building blocks of arguments and relations between them are known, unlike in QBAFs, where arguments are abstract entities. Differently from existing approaches, our methodology accommodates incomplete information about arguments' premises. We demonstrate the potential of our approach by introducing two different instantiations of the methodology, leveraging existing gradual semantics for QBAFs in these more complex frameworks. We also define a set of novel properties for gradual semantics in structured argumentation, discuss their suitability over a set of existing properties. Finally, we provide a comprehensive theoretical analysis assessing the instantiations, demonstrating the their advantages over existing gradual semantics for QBAFs and structured argumentation.

PeerArg: Argumentative Peer Review with LLMs

Peer review is an essential process to determine the quality of papers submitted to scientific conferences or journals. However, it is subjective and prone to biases. Several studies have been conducted to apply techniques from NLP to support peer review, but they are based on black-box techniques and their outputs are difficult to interpret and trust. In this paper, we propose a novel pipeline to support and understand the reviewing and decision-making processes of peer review: the PeerArg system combining LLMs with methods from knowledge representation. PeerArg takes in input a set of reviews for a paper and outputs the paper acceptance prediction. We evaluate the performance of the PeerArg pipeline on three different datasets, in comparison with a novel end-2-end LLM that uses few-shot learning to predict paper acceptance given reviews. The results indicate that the end-2-end LLM is capable of predicting paper acceptance from reviews, but a variant of the PeerArg pipeline outperforms this LLM.

Applying Attribution Explanations in Truth-Discovery Quantitative Bipolar Argumentation Frameworks

Explaining the strength of arguments under gradual semantics is receiving increasing attention. For example, various studies in the literature offer explanations by computing the attribution scores of arguments or edges in Quantitative Bipolar Argumentation Frameworks (QBAFs). These explanations, known as Argument Attribution Explanations (AAEs) and Relation Attribution Explanations (RAEs), commonly employ removal-based and Shapley-based techniques for computing the attribution scores. While AAEs and RAEs have proven useful in several applications with acyclic QBAFs, they remain largely unexplored for cyclic QBAFs. Furthermore, existing applications tend to focus solely on either AAEs or RAEs, but do not compare them directly. In this paper, we apply both AAEs and RAEs, to Truth Discovery QBAFs (TD-QBAFs), which assess the trustworthiness of sources (e.g., websites) and their claims (e.g., the severity of a virus), and feature complex cycles. We find that both AAEs and RAEs can provide interesting explanations and can give non-trivial and surprising insights.

Exploring the Effect of Explanation Content and Format on User Comprehension and Trust

In recent years, various methods have been introduced for explaining the outputs of "black-box" AI models. However, it is not well understood whether users actually comprehend and trust these explanations. In this paper, we focus on explanations for a regression tool for assessing cancer risk and examine the effect of the explanations' content and format on the user-centric metrics of comprehension and trust. Regarding content, we experiment with two explanation methods: the popular SHAP, based on game-theoretic notions and thus potentially complex for everyday users to comprehend, and occlusion-1, based on feature occlusion which may be more comprehensible. Regarding format, we present SHAP explanations as charts (SC), as is conventional, and occlusion-1 explanations as charts (OC) as well as text (OT), to which their simpler nature also lends itself. The experiments amount to user studies questioning participants, with two different levels of expertise (the general population and those with some medical training), on their subjective and objective comprehension of and trust in explanations for the outputs of the regression tool. In both studies we found a clear preference in terms of subjective comprehension and trust for occlusion-1 over SHAP explanations in general, when comparing based on content. However, direct comparisons of explanations when controlling for format only revealed evidence for OT over SC explanations in most cases, suggesting that the dominance of occlusion-1 over SHAP explanations may be driven by a preference for text over charts as explanations. Finally, we found no evidence of a difference between the explanation types in terms of objective comprehension. Thus overall, the choice of the content and format of explanations needs careful attention, since in some contexts format, rather than content, may play the critical role in improving user experience.

Learning Brave Assumption-Based Argumentation Frameworks via ASP

Assumption-based Argumentation (ABA) is advocated as a unifying formalism for various forms of non-monotonic reasoning, including logic programming. It allows capturing defeasible knowledge, subject to argumentative debate. While, in much existing work, ABA frameworks are given up-front, in this paper we focus on the problem of automating their learning from background knowledge and positive/negative examples. Unlike prior work, we newly frame the problem in terms of brave reasoning under stable extensions for ABA. We present a novel algorithm based on transformation rules (such as Rote Learning, Folding, Assumption Introduction and Fact Subsumption) and an implementation thereof that makes use of Answer Set Programming. Finally, we compare our technique to state-of-the-art ILP systems that learn defeasible knowledge.

Preference-Based Abstract Argumentation for Case-Based Reasoning (with Appendix)

In the pursuit of enhancing the efficacy and flexibility of interpretable, data-driven classification models, this work introduces a novel incorporation of user-defined preferences with Abstract Argumentation and Case-Based Reasoning (CBR). Specifically, we introduce Preference-Based Abstract Argumentation for Case-Based Reasoning (which we call AA-CBR-P), allowing users to define multiple approaches to compare cases with an ordering that specifies their preference over these comparison approaches. We prove that the model inherently follows these preferences when making predictions and show that previous abstract argumentation for case-based reasoning approaches are insufficient at expressing preferences over constituents of an argument. We then demonstrate how this can be applied to a real-world medical dataset sourced from a clinical trial evaluating differing assessment methods of patients with a primary brain tumour. We show empirically that our approach outperforms other interpretable machine learning models on this dataset.

CE-QArg: Counterfactual Explanations for Quantitative Bipolar Argumentation Frameworks (Technical Report)

There is a growing interest in understanding arguments' strength in Quantitative Bipolar Argumentation Frameworks (QBAFs). Most existing studies focus on attribution-based methods that explain an argument's strength by assigning importance scores to other arguments but fail to explain how to change the current strength to a desired one. To solve this issue, we introduce counterfactual explanations for QBAFs. We discuss problem variants and propose an iterative algorithm named Counterfactual Explanations for Quantitative bipolar Argumentation frameworks (CE-QArg). CE-QArg can identify valid and cost-effective counterfactual explanations based on two core modules, polarity and priority, which help determine the updating direction and magnitude for each argument, respectively. We discuss some formal properties of our counterfactual explanations and empirically evaluate CE-QArg on randomly generated QBAFs.

Heterogeneous Graph Neural Networks with Post-hoc Explanations for Multi-modal and Explainable Land Use Inference

Urban land use inference is a critically important task that aids in city planning and policy-making. Recently, the increased use of sensor and location technologies has facilitated the collection of multi-modal mobility data, offering valuable insights into daily activity patterns. Many studies have adopted advanced data-driven techniques to explore the potential of these multi-modal mobility data in land use inference. However, existing studies often process samples independently, ignoring the spatial correlations among neighbouring objects and heterogeneity among different services. Furthermore, the inherently low interpretability of complex deep learning methods poses a significant barrier in urban planning, where transparency and extrapolability are crucial for making long-term policy decisions. To overcome these challenges, we introduce an explainable framework for inferring land use that synergises heterogeneous graph neural networks (HGNs) with Explainable AI techniques, enhancing both accuracy and explainability. The empirical experiments demonstrate that the proposed HGNs significantly outperform baseline graph neural networks for all six land-use indicators, especially in terms of 'office' and 'sustenance'. As explanations, we consider feature attribution and counterfactual explanations. The analysis of feature attribution explanations shows that the symmetrical nature of the `residence' and 'work' categories predicted by the framework aligns well with the commuter's 'work' and 'recreation' activities in London. The analysis of the counterfactual explanations reveals that variations in node features and types are primarily responsible for the differences observed between the predicted land use distribution and the ideal mixed state. These analyses demonstrate that the proposed HGNs can suitably support urban stakeholders in their urban planning and policy-making.

Analyzing Key Neurons in Large Language Models

Large Language Models (LLMs) possess vast amounts of knowledge within their parameters, prompting research into methods for locating and editing this knowledge. Previous investigations have primarily focused on fill-in-the-blank tasks and locating entity-related usually single-token facts) information in relatively small-scale language models. However, several key questions remain unanswered: (1) How can we effectively locate query-relevant neurons in contemporary autoregressive LLMs, such as LLaMA and Mistral? (2) How can we address the challenge of long-form text generation? (3) Are there localized knowledge regions in LLMs? In this study, we introduce Neuron Attribution-Inverse Cluster Attribution (NA-ICA), a novel architecture-agnostic framework capable of identifying key neurons in LLMs. NA-ICA allows for the examination of long-form answers beyond single tokens by employing the proxy task of multi-choice question answering. To evaluate the effectiveness of our detected key neurons, we construct two multi-choice QA datasets spanning diverse domains and languages. Empirical evaluations demonstrate that NA-ICA outperforms baseline methods significantly. Moreover, analysis of neuron distributions reveals the presence of visible localized regions, particularly within different domains. Finally, we demonstrate the potential applications of our detected key neurons in knowledge editing and neuron-based prediction.

Identifiable Object-Centric Representation Learning via Probabilistic Slot Attention

Learning modular object-centric representations is crucial for systematic generalization. Existing methods show promising object-binding capabilities empirically, but theoretical identifiability guarantees remain relatively underdeveloped. Understanding when object-centric representations can theoretically be identified is crucial for scaling slot-based methods to high-dimensional images with correctness guarantees. To that end, we propose a probabilistic slot-attention algorithm that imposes an aggregate mixture prior over object-centric slot representations, thereby providing slot identifiability guarantees without supervision, up to an equivalence relation. We provide empirical verification of our theoretical identifiability result using both simple 2-dimensional data and high-resolution imaging datasets.