Explanation Generation
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Benchmarks
Most implemented
Using Stratified Sampling to Improve LIME Image Explanations
MACRec: a Multi-Agent Collaboration Framework for Recommendation
Explaining black box text modules in natural language with language models
TE2Rules: Explaining Tree Ensembles using Rules
Papers
Selecting The Most Informative Tokens in Natural Language Autoencoders
Natural language autoencoders translate a language model's internal activations into readable explanations. Explaining every token position is costly. Which positions should an auditor inspect to understand a potential t…
Explanation GenerationOrder Matters: A Chinese Multi-Panel Meme Benchmark for Vision-Language Reasoning
Many multimodal tasks depend on how visual elements are ordered and composed, not only on recognizing them in isolation. Internet memes are a compact case of this problem: their punchline often depends on a constrained r…
Explanation GenerationMultimodal ReasoningPATE-Forensics: Perception-as-Tool for Explainable Deepfake Forensics with General-Purpose MLLMs
Existing explainable deepfake forensic methods typically rely on task-adapted MLLM to jointly address detection, localization, and explanation. Inspired by agent-style tool use, we instead introduce a Perception-as-Tool …
Explanation GenerationComputational KJ-Ho: An Analyst-Bias-Free Insight Extraction Framework from Large-Scale Qualitative Data Using Domain-Specialized LLMs
The qualitative research methodologies that underpin consumer-insight generation - the KJ method, Grounded Theory, and Thematic Analysis - share a structural constraint: the cognitive processing capacity of the human ana…
Explanation GenerationArtECulture: Benchmarking Culture-Conditioned Visual Emotion Understanding in Multimodal Large Language Models
Existing visual emotion understanding methods typically ignore cultural variations in emotional perception. We introduce culture-conditioned visual emotion understanding, a task that predicts the culture-specific emotion…
Explanation GenerationHalluTruthQA-4K: A Fine-Grained Corpus and Annotation Process for Arabic Hallucination Detection and Truth Verification
Large language models can generate fluent Arabic answers while introducing factual errors that are difficult to identify and verify. Existing Arabic hallucination resources often assign a binary label to an entire respon…
Explanation GenerationQuestion SelectionAnswer Generation