GLARE: A Natural Language Interface for Querying Global Explanations

• URL: http://arxiv.org/abs/2606.19735v1
• Date: Thu, 18 Jun 2026 02:58:35 GMT
• Title: GLARE: A Natural Language Interface for Querying Global Explanations
• Abstract summary: The system's core LLM acts as a mediator, translating natural language questions into structured queries over local explanation data.<n>We evaluate the system on intent interpretation, query mapping accuracy, generalization to novel queries and datasets, and robustness to linguistic errors.
• Score: 0.6445605125467574
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While global explanations are crucial for understanding vision models across datasets, classes, and decision contexts, their complex and monolithic nature often hinders practical exploration. Because users typically seek targeted answers to specific questions rather than static artifacts, we present an LLM-based interactive interface that provides natural language access to global explanations for black-box image classifiers. The system's core LLM acts as a mediator, translating natural language questions into structured SQL queries over local explanation data. This enables flexible aggregation without exposing users to low-level representations. For each query, the interface outputs statistics-augmented natural language responses, supporting local explanations, and intent-aligned visualizations. We evaluate the system on intent interpretation, query mapping accuracy, generalization to novel queries and datasets, and robustness to linguistic errors. Our results demonstrate that LLM-mediated querying substantially improves the accessibility and usability of global explanations for human-centered XAI.

Related papers

• A Human-in-the-Loop, LLM-Centered Architecture for Knowledge-Graph Question Answering [41.99844472131922]
  Large Language Models excel at language understanding but are limited in knowledge-intensive domains.<n>This work introduces an interactive framework in which LLMs generate and explain Cypher graph queries.
  arXiv  Detail & Related papers  (2026-02-05T10:10:19Z)
• OsmT: Bridging OpenStreetMap Queries and Natural Language with Open-source Tag-aware Language Models [30.29148893930439]
  We present OsmT, an open-source tag-aware language model for bridging natural language and structured query languages.<n>We introduce a Tag Retrieval Augmentation (TRA) mechanism that incorporates contextually relevant tag knowledge into the generation process.<n>We also define a reverse task, OverpassQL-to-Text, which translates structured queries into natural language explanations.
  arXiv  Detail & Related papers  (2025-12-04T12:24:36Z)
• Metaphor and Large Language Models: When Surface Features Matter More than Deep Understanding [6.0158981171030685]
  This paper presents a comprehensive evaluation of the capabilities of Large Language Models (LLMs) in metaphor interpretation across multiple datasets, tasks, and prompt configurations.<n>We address these limitations by conducting extensive experiments using diverse publicly available datasets with inference and metaphor annotations.<n>The results indicate that LLMs' performance is more influenced by features like lexical overlap and sentence length than by metaphorical content.
  arXiv  Detail & Related papers  (2025-07-21T08:09:11Z)
• Beyond Memorization: Assessing Semantic Generalization in Large Language Models Using Phrasal Constructions [3.0906699069248806]
  Construction Grammar (CxG) is a psycholinguistically grounded framework for testing generalization.<n>Our dataset consists of English phrasal constructions, for which speakers are known to be able to abstract over commonplace instantiations.<n>Our results demonstrate that state-of-the-art models, including GPT-o1, exhibit a performance drop of over 40% on our second task.
  arXiv  Detail & Related papers  (2025-01-08T18:15:10Z)
• Language Agents Meet Causality -- Bridging LLMs and Causal World Models [50.79984529172807]
  We propose a framework that integrates causal representation learning with large language models. This framework learns a causal world model, with causal variables linked to natural language expressions. We evaluate the framework on causal inference and planning tasks across temporal scales and environmental complexities.
  arXiv  Detail & Related papers  (2024-10-25T18:36:37Z)
• INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuning [59.07490387145391]
  Large language models (LLMs) have demonstrated impressive capabilities in various natural language processing tasks. Their application to information retrieval (IR) tasks is still challenging due to the infrequent occurrence of many IR-specific concepts in natural language. We introduce a novel instruction tuning dataset, INTERS, encompassing 20 tasks across three fundamental IR categories.
  arXiv  Detail & Related papers  (2024-01-12T12:10:28Z)
• DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text [73.68051228972024]
  Large Language Models (LLMs) have exhibited impressive generation capabilities, but they suffer from hallucinations when relying on their internal knowledge. Retrieval-augmented LLMs have emerged as a potential solution to ground LLMs in external knowledge.
  arXiv  Detail & Related papers  (2023-10-31T04:37:57Z)
• An In-Context Schema Understanding Method for Knowledge Base Question Answering [70.87993081445127]
  Large Language Models (LLMs) have shown strong capabilities in language understanding and can be used to solve this task. Existing methods bypass this challenge by initially employing LLMs to generate drafts of logic forms without schema-specific details. We propose a simple In-Context Understanding (ICSU) method that enables LLMs to directly understand schemas by leveraging in-context learning.
  arXiv  Detail & Related papers  (2023-10-22T04:19:17Z)
• Can Large Language Models Understand Real-World Complex Instructions? [54.86632921036983]
  Large language models (LLMs) can understand human instructions, but struggle with complex instructions. Existing benchmarks are insufficient to assess LLMs' ability to understand complex instructions. We propose CELLO, a benchmark for evaluating LLMs' ability to follow complex instructions systematically.
  arXiv  Detail & Related papers  (2023-09-17T04:18:39Z)
• Query Understanding in the Age of Large Language Models [6.630482733703617]
  We describe a generic framework for interactive query-rewriting using large-language models (LLM) A key aspect of our framework is the ability of the rewriter to fully specify the machine intent by the search engine in natural language. We detail the concept, backed by initial experiments, along with open questions for this interactive query understanding framework.
  arXiv  Detail & Related papers  (2023-06-28T08:24:14Z)
• Explaining Patterns in Data with Language Models via Interpretable Autoprompting [143.4162028260874]
  We introduce interpretable autoprompting (iPrompt), an algorithm that generates a natural-language string explaining the data. iPrompt can yield meaningful insights by accurately finding groundtruth dataset descriptions. Experiments with an fMRI dataset show the potential for iPrompt to aid in scientific discovery.
  arXiv  Detail & Related papers  (2022-10-04T18:32:14Z)
• GreaseLM: Graph REASoning Enhanced Language Models for Question Answering [159.9645181522436]
  GreaseLM is a new model that fuses encoded representations from pretrained LMs and graph neural networks over multiple layers of modality interaction operations. We show that GreaseLM can more reliably answer questions that require reasoning over both situational constraints and structured knowledge, even outperforming models 8x larger.
  arXiv  Detail & Related papers  (2022-01-21T19:00:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.