The Shape of Uncertainty: Dissecting Confidence in Historical Garments

Author: Denis Avetisyan

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New research breaks down how uncertainty affects the accurate reconstruction of historical clothing, specifically focusing on the challenges presented by bodices.

This paper decomposes epistemic uncertainty into per-class contributions to better understand confidence levels in historical garment reconstruction.

While Bayesian deep learning offers powerful tools for quantifying uncertainty, standard approaches often collapse epistemic uncertainty into a single scalar, obscuring critical distinctions between potentially erroneous classifications. The paper ‘Not Just How Much, But Where: Decomposing Epistemic Uncertainty into Per-Class Contributions’ addresses this limitation by decomposing mutual information into a per-class vector, C_k(x)=\sigma_k^{2}/(2μ_k), revealing where a model’s ignorance lies. This decomposition not only improves selective prediction and out-of-distribution detection-reducing selective risk by 34.7% in diabetic retinopathy-but also demonstrates the importance of posterior approximation quality in shaping uncertainty estimates. Does this per-class decomposition offer a more nuanced understanding of model confidence, and could it unlock more robust and reliable safety-critical applications of deep learning?

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The Fragility of Fluency: Beyond Pattern Matching

Despite their proficiency in crafting human-quality text, Large Language Models frequently exhibit shortcomings in factual grounding and logical consistency. These models, trained on vast datasets, excel at identifying patterns and generating statistically probable sequences of words, but this process doesn’t inherently guarantee truthfulness. A model can confidently articulate a plausible-sounding statement that is, in fact, demonstrably false, or construct an argument riddled with subtle fallacies. This disconnect arises because the models prioritize fluency and coherence over genuine understanding, often relying on superficial correlations rather than deep causal relationships. Consequently, while they can mimic intelligent discourse, their reasoning abilities remain brittle and prone to error, necessitating careful scrutiny of their outputs and ongoing research into methods for enhancing their reliability.

Large language models, despite their impressive ability to generate human-quality text, fundamentally rely on parametric knowledge – facts and relationships embedded directly within the model’s billions of numerical weights established during training. This presents a significant limitation, as this knowledge is essentially frozen at the time of training and cannot be updated without retraining the entire model – a computationally expensive and time-consuming process. Consequently, the model’s understanding of the world becomes increasingly outdated, susceptible to inaccuracies, and unable to reflect newly discovered information or evolving perspectives. This inherent static nature restricts the model’s adaptability and poses a substantial challenge to its long-term reliability, particularly in domains where knowledge is constantly changing.

The inherent limitations of Large Language Models stem, in part, from their dependence on knowledge embedded during training; this static “parametric knowledge” quickly becomes outdated and hinders reliable performance. Consequently, significant research focuses on equipping these models with the ability to access and integrate information from external knowledge sources – databases, websites, or real-time data feeds. This dynamic knowledge incorporation allows for responses grounded in current information, rather than potentially obsolete data locked within the model’s architecture. Such methods not only improve factual accuracy but also offer a pathway to greater trustworthiness, as the model can, in principle, cite its sources and adapt to evolving understandings of the world – moving beyond simply generating text to genuinely reasoning with information.

Augmenting Intelligence: The Promise of Retrieval-Augmented Generation

Retrieval-Augmented Generation (RAG) addresses limitations of Large Language Models (LLMs) by supplementing their pre-trained parameters with information retrieved from external sources during inference. Rather than relying solely on internally stored knowledge, RAG systems first identify relevant documents or data fragments from a Knowledge Source – which can include databases, websites, or other structured or unstructured data – based on the user’s input. This retrieved information is then incorporated into the prompt provided to the LLM, effectively conditioning the model’s output on external evidence. This process allows LLMs to generate responses grounded in factual data, expanding their knowledge base beyond the data used during their initial training and enabling responses to queries outside of that initial scope.

The initial stage of Retrieval-Augmented Generation involves identifying and extracting pertinent information from a Knowledge Base. This retrieval process leverages technologies such as Vector Databases, which store data as high-dimensional vectors to enable efficient similarity searches, and Embedding Models. These models transform text into numerical vector representations, allowing the system to assess the semantic relevance of documents within the Knowledge Base to a given query. By calculating the distance between the query’s embedding and the embeddings of documents in the Vector Database, the system can identify and retrieve the most relevant contextual information for subsequent text generation.

Retrieval-Augmented Generation (RAG) addresses the issue of factual inaccuracy, commonly referred to as ‘hallucination’, in Large Language Models (LLMs) by incorporating external knowledge during text generation. LLMs, trained on extensive datasets, can sometimes generate plausible but incorrect statements. RAG mitigates this by first retrieving relevant documents or data snippets from a designated knowledge source based on the user’s prompt. This retrieved information is then provided as context to the LLM before generating a response, effectively grounding the output in verifiable evidence. Consequently, the generated text is more likely to be factually consistent and attributable to the provided knowledge source, increasing the overall faithfulness and reliability of the LLM’s output.

The Architecture of Accuracy: Optimizing the RAG Pipeline

The Retrieval-Augmented Generation (RAG) pipeline’s overall performance is directly proportional to the quality of each stage, beginning with the initial query formulation and extending through knowledge retrieval and culminating in final text generation. A poorly formulated query can yield irrelevant knowledge sources, negatively impacting retrieval accuracy. Similarly, inefficiencies in the retrieval process – such as slow indexing or ineffective similarity searches – introduce latency and reduce the scope of relevant context. Finally, the generative model’s capacity to synthesize retrieved information into a coherent and accurate response is paramount; limitations in this stage can lead to hallucinations or factually incorrect outputs, even with high-quality retrieval. Therefore, optimizing each component of the RAG pipeline-query processing, knowledge retrieval, and text generation-is essential for maximizing the effectiveness of the system.

Query expansion and relevance scoring are fundamental components in optimizing Retrieval-Augmented Generation (RAG) systems for knowledge source identification. Query expansion involves reformulating the initial user query into multiple related queries, increasing the likelihood of matching relevant documents within the knowledge base. This can be achieved through synonym replacement, hypernym/hyponym expansion, or related keyword generation. Relevance scoring, typically implemented using techniques like vector similarity search (e.g., cosine similarity with embeddings generated from models like Sentence Transformers), then ranks these candidate knowledge sources based on their semantic similarity to the expanded query. Higher scores indicate a stronger relevance, allowing the RAG pipeline to prioritize the most pertinent information for subsequent processing and text generation. Effective implementation of both techniques directly impacts the accuracy and quality of the RAG system’s outputs by reducing noise and increasing the signal from the knowledge base.

Large Language Models (LLMs) possess a finite context window, limiting the amount of text they can process in a single input. To overcome this, Context Compression techniques are employed to reduce the length of retrieved knowledge sources while retaining critical information. Methods include sentence selection, summarization, and keyword extraction, prioritizing content relevant to the initial query. Effective compression strategies minimize information loss by identifying and preserving key entities, relationships, and facts. Lossy compression techniques, while reducing length more aggressively, require careful evaluation to ensure minimal impact on downstream task performance. The goal is to fit a sufficient amount of relevant context within the LLM’s window, enabling accurate and informed responses without truncating essential data.

Long Context Models (LCMs) are a developing class of Large Language Models (LLMs) engineered to process significantly larger input sequences than traditional models. Standard LLMs are constrained by their context window – the maximum number of tokens they can accept as input – typically ranging from 2,000 to 8,000 tokens. LCMs, however, have demonstrated capabilities exceeding 100,000 tokens, and in some cases reaching over a million. This expanded context window enables the model to retain and utilize information from substantially larger knowledge sources within a Retrieval-Augmented Generation (RAG) pipeline, potentially improving accuracy and reducing the need for extensive context compression or filtering techniques. Architecturally, LCMs achieve this through innovations in attention mechanisms, such as sparse attention or linear attention, which reduce the computational complexity associated with processing long sequences.

Beyond Static Knowledge: The Expanding Horizon of Intelligent Systems

Retrieval-Augmented Generation (RAG) offers a compelling pathway to bolster the reliability of Large Language Models, particularly in scenarios demanding factual accuracy. By grounding responses in retrieved, verified information, RAG minimizes the risk of “hallucinations”-the generation of plausible but incorrect statements-that often plague these models. This fidelity is crucial for applications like question answering, where users require dependable answers, and content creation, where maintaining accuracy is paramount. The ability to directly link generated text to its source material also fosters greater user trust and allows for easy verification of claims, ultimately paving the way for more responsible and effective AI deployments.

Retrieval-Augmented Generation uniquely positions Large Language Models for continuous learning and adaptation without the computationally expensive process of full retraining. Unlike traditional models requiring updates to all parameters when new information arises, RAG systems dynamically integrate knowledge from external sources during the generation process. This approach allows the model to access and utilize current data – such as recent events or newly published research – without altering its core architecture or learned weights. Consequently, the system can evolve its responses and maintain accuracy over time, effectively mirroring a human’s ability to learn and incorporate new facts into their existing knowledge base. This capacity for on-the-fly knowledge integration promises a significant advantage in dynamic fields where information is constantly changing, fostering more reliable and up-to-date AI applications.

Ongoing investigation centers on refining how information is retrieved for Retrieval-Augmented Generation (RAG) systems, moving beyond simple keyword searches to nuanced semantic understanding. Researchers are actively developing strategies to pinpoint the most relevant knowledge, even when expressed indirectly, and are simultaneously tackling the challenge of ‘context compression’ – efficiently distilling lengthy documents into concise summaries without losing crucial detail. Further exploration extends to novel architectural designs capable of processing significantly larger contexts, enabling models to draw connections across vast amounts of information and ultimately improve the coherence and accuracy of generated text. These combined efforts promise to unlock the full potential of RAG, allowing it to seamlessly integrate and leverage the ever-expanding digital knowledge base.

Retrieval-Augmented Generation (RAG) signifies a fundamental advancement in artificial intelligence, moving beyond models that rely solely on pre-existing parameters. This approach enables AI systems to dynamically access and integrate information from external knowledge sources, effectively bridging the gap between static data and real-world understanding. By grounding responses in verifiable evidence, RAG not only improves the accuracy and relevance of generated content but also fosters greater transparency and trustworthiness. This capability is crucial for deploying AI in sensitive applications where reliability is paramount, and it unlocks the potential for AI to continually learn and adapt to an ever-expanding universe of information, ultimately paving the way for more intelligent and dependable AI systems.

The study of garment construction, as exemplified by the detailed examination of the ‘bodice’ within this paper, reveals how knowledge is not simply found but actively produced through meticulous deconstruction and analysis. This echoes Michel Foucault’s assertion: “There is no power relation without resistance.” Each seam, each historical record, presents a power dynamic – the maker imposing form, the wearer negotiating comfort, and the historian interpreting intent. The paper’s decomposition of epistemic uncertainty into per-class contributions mirrors a Foucauldian excavation, uncovering the underlying structures that shape our understanding of historical textiles and, by extension, the social forces embedded within them. It is a reminder that seemingly objective analysis always operates within a framework of constructed knowledge.

What Lies Ahead?

The decomposition of epistemic uncertainty, as demonstrated through the focused lens of garment construction-specifically, the bodice-reveals a crucial, if subtly unsettling, truth. It is not merely that uncertainty exists, but where it resides within the reconstruction process that demands attention. To quantify uncertainty per class-to pinpoint precisely which elements of a historical garment are most susceptible to interpretive error-is a technically sound advance. However, the implications extend beyond improved accuracy in digital modeling. Each identified locus of uncertainty encodes a prior assumption, a methodological bias, and ultimately, a particular worldview regarding the past.

Future work must address the ethical dimension of this precision. The ability to isolate uncertainty invites a dangerous form of historical control. What interpretive choices are being masked by a seemingly objective quantification? What narratives are subtly reinforced, or silenced, by the prioritization of certain features over others? A rigorous accounting of uncertainty is only valuable if paired with an equally rigorous self-assessment of the values informing that accounting.

The field should move beyond simply measuring uncertainty to actively interrogating it. Further research could explore the impact of diverse interpretive frameworks on the distribution of uncertainty, or the development of methods for explicitly visualizing and challenging the underlying assumptions embedded within reconstruction algorithms. Progress, in this domain, demands not only technical sophistication, but a profound awareness of the responsibility inherent in shaping our understanding of material culture.

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Original article: https://arxiv.org/pdf/2602.21160.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-02-26 03:10