arXiv subjects

Guancheng Zhou

Publications and source records attributed to Guancheng Zhou.

7 recordsLinked to original sources

arXiv2026

Correcting Variable Importance Scored by Random Forests

Variable importance produced by Random Forests (RF) is used widely in statistical data analysis, and has played an important role in a variety of tasks such as assisting model interpretation, model selection and diagnosis, and cost-bounded learning etc. However, the calculation of variable importance in RF does not take into account of the correlations among variables, and variables that are correlated to many other variables tend to receive a lower importance index or being completely masked (i.e., with an importance index near zero) by other strongly correlated variables. To prevent influence from unwanted correlated variables in calculating variable importance, we propose to group variables by their conditional correlations (conditional on the response variable). We explore two computationally efficient options, with one grouping variables individually, and then separates the variable of interest from all correlated variables, while the other uses clustering to group variables according to their pair-wise conditional correlations. Our experiments show that both lead to sensible corrections to the importance of variables.

stat.ME↗

arXiv2026

A Distributional View for Visual Mechanistic Interpretability: KL-Minimal Soft-Constraint Principle

Most current paradigms in visual mechanistic interpretability (MI) remain confined to interpreting internal units of the vision model via heuristic methods (e.g., top-$K$ activation retrieval or optimization with regularization). In this work, we establish a theoretical distributional view for visual MI, which models the influence of a feature activation on the natural image distribution, thereby formulating a Kullback-Leibler (KL)-minimal optimization problem to model the MI task. Under this framework, statistical biases are identified within previous MI paradigms, which reveal that they may either be perceptually uninterpretable to humans (i.e., deviate from the natural image distribution), or mechanistically unfaithful to the vision models (i.e., unable to activate model features). To resolve the biases under the distributional view, we propose a model with a KL-minimal soft-constraint principle for visual MI that theoretically balances interpretability and faithfulness. We realize this principle via energy-guided diffusion posterior sampling. Extensive experiments validate the theoretical soundness of the proposed distributional view and demonstrate the practical effectiveness of our paradigm on the DINOv3 vision model.

cs.CV↗

arXiv2026

Tracing the Thought of a Grandmaster-level Chess-Playing Transformer

While modern transformer neural networks achieve grandmaster-level performance in chess and other reasoning tasks, their internal computation process remains largely opaque. Focusing on Leela Chess Zero (LC0), we introduce a sparse decomposition framework to interpret its internal computation by decomposing its MLP and attention modules with sparse replacement layers, which capture the primary computation process of LC0. We conduct a detailed case study showing that these pathways expose rich, interpretable tactical considerations that are empirically verifiable. We further introduce three quantitative metrics and show that LC0 exhibits parallel reasoning behavior consistent with the inductive bias of its policy head architecture. To the best of our knowledge, this is the first work to decompose the internal computation of a transformer on both MLP and attention modules for interpretability. Combining sparse replacement layers and causal interventions in LC0 provides a comprehensive understanding of advanced tactical reasoning, offering critical insights into the underlying mechanisms of superhuman systems. Our code is available at https://github.com/JacklE0niden/Leela-SAEs.

cs.LG↗

arXiv2025

Critical Challenges in Content Moderation for People Who Use Drugs (PWUD): Insights into Online Harm Reduction Practices from Moderators

Online communities serve as essential support channels for People Who Use Drugs (PWUD), providing access to peer support and harm reduction information. The moderation of these communities involves consequential decisions affecting member safety, yet existing sociotechnical systems provide insufficient support for moderators. Through interviews with experienced moderators from PWUD forums on Reddit, we examine the unique nature of this work and its implications for HCI and content moderation research. We demonstrate that this work constitutes a distinct form of public health intervention characterised by three challenges: (1) high-stakes risk evaluation requiring pharmacological expertise, (2) time-critical crisis intervention spanning platform content and external drug market surveillance, and (3) navigation of structural conflicts where platform policies designed to minimise legal liability directly oppose community harm reduction goals. Our findings extend existing HCI moderation frameworks by revealing how legal liability structures can systematically undermine expert moderators' protective work, with implications for other marginalised communities facing similar regulatory tensions, including abortion care and sex work contexts. We identify two necessary shifts in sociotechnical design: moving from binary classification to multi-dimensional approaches that externalise competing factors moderators must balance, and shifting from low-level rule programming to high-level example-based instruction. However, we surface unresolved tensions around volunteer labour sustainability and risks of incorporating automated systems in high-stakes health contexts, identifying open questions requiring HCI research attention. These findings inform the design of platforms that better accommodate vulnerable populations whose health needs conflict with regulatory frameworks.

cs.HC↗

arXiv2025

A Computational Approach to Improving Fairness in K-means Clustering

The popular K-means clustering algorithm potentially suffers from a major weakness for further analysis or interpretation. Some cluster may have disproportionately more (or fewer) points from one of the subpopulations in terms of some sensitive variable, e.g., gender or race. Such a fairness issue may cause bias and unexpected social consequences. This work attempts to improve the fairness of K-means clustering with a two-stage optimization formulation--clustering first and then adjust cluster membership of a small subset of selected data points. Two computationally efficient algorithms are proposed in identifying those data points that are expensive for fairness, with one focusing on nearest data points outside of a cluster and the other on highly 'mixed' data points. Experiments on benchmark datasets show substantial improvement on fairness with a minimal impact to clustering quality. The proposed algorithms can be easily extended to a broad class of clustering algorithms or fairness metrics.

cs.LG↗

arXiv2024

Less is More: Making Smaller Language Models Competent Subgraph Retrievers for Multi-hop KGQA

Retrieval-Augmented Generation (RAG) is widely used to inject external non-parametric knowledge into large language models (LLMs). Recent works suggest that Knowledge Graphs (KGs) contain valuable external knowledge for LLMs. Retrieving information from KGs differs from extracting it from document sets. Most existing approaches seek to directly retrieve relevant subgraphs, thereby eliminating the need for extensive SPARQL annotations, traditionally required by semantic parsing methods. In this paper, we model the subgraph retrieval task as a conditional generation task handled by small language models. Specifically, we define a subgraph identifier as a sequence of relations, each represented as a special token stored in the language models. Our base generative subgraph retrieval model, consisting of only 220M parameters, achieves competitive retrieval performance compared to state-of-the-art models relying on 7B parameters, demonstrating that small language models are capable of performing the subgraph retrieval task. Furthermore, our largest 3B model, when plugged with an LLM reader, sets new SOTA end-to-end performance on both the WebQSP and CWQ benchmarks. Our model and data will be made available online: https://github.com/hwy9855/GSR.

cs.CL↗

arXiv2024

Prompting Large Language Models with Knowledge Graphs for Question Answering Involving Long-tail Facts

Although Large Language Models (LLMs) are effective in performing various NLP tasks, they still struggle to handle tasks that require extensive, real-world knowledge, especially when dealing with long-tail facts (facts related to long-tail entities). This limitation highlights the need to supplement LLMs with non-parametric knowledge. To address this issue, we analysed the effects of different types of non-parametric knowledge, including textual passage and knowledge graphs (KGs). Since LLMs have probably seen the majority of factual question-answering datasets already, to facilitate our analysis, we proposed a fully automatic pipeline for creating a benchmark that requires knowledge of long-tail facts for answering the involved questions. Using this pipeline, we introduce the LTGen benchmark. We evaluate state-of-the-art LLMs in different knowledge settings using the proposed benchmark. Our experiments show that LLMs alone struggle with answering these questions, especially when the long-tail level is high or rich knowledge is required. Nonetheless, the performance of the same models improved significantly when they were prompted with non-parametric knowledge. We observed that, in most cases, prompting LLMs with KG triples surpasses passage-based prompting using a state-of-the-art retriever. In addition, while prompting LLMs with both KG triples and documents does not consistently improve knowledge coverage, it can dramatically reduce hallucinations in the generated content.

cs.CL↗