NARRA-Gym is an executable benchmark that generates complete interactive narrative episodes from emotional seeds and logs full model trajectories to expose gaps in coherence, adaptation, and personalization that static story tests miss.
Can Large Language Models Be an Alternative to Human Evaluations?
8 Pith papers cite this work. Polarity classification is still indexing.
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2026 8verdicts
UNVERDICTED 8representative citing papers
Neuron Auctions auction continuous neuron intervention budgets on brand-specific orthogonal subspaces in LLMs to achieve strategy-proof revenue optimization while penalizing user utility loss.
LLMs exhibit positional bias and context-dependent scoring patterns when judging document similarity, with each model showing a stable scoring fingerprint but a shared hierarchy of sensitivity to different semantic perturbations.
RUBEN discovers minimal rule sets explaining RAG LLM outputs via novel pruning and applies them to evaluate LLM safety against adversarial injections.
Sycophancy is a boundary failure between social alignment and epistemic integrity, captured by a three-condition framework plus taxonomy of targets, mechanisms, and severity.
Automatic prompt optimization using lenient LLM judges improves performance and transferability in legal QA evaluations compared to human design or strict judges.
G-Defense builds claim-centered graphs from sub-claims, applies RAG for evidence and competing explanations, then uses graph inference to detect fake news veracity and generate intuitive explanation graphs, claiming SOTA results.
Domain-adapted utterance-level retrieval raises Cohen's kappa for tutoring dialogue act annotation to 0.526-0.580 on TalkMoves and 0.659-0.743 on Eedi, beating no-retrieval baselines by large margins across three LLMs.
citing papers explorer
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NARRA-Gym for Evaluating Interactive Narrative Agents
NARRA-Gym is an executable benchmark that generates complete interactive narrative episodes from emotional seeds and logs full model trajectories to expose gaps in coherence, adaptation, and personalization that static story tests miss.
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LLM Advertisement based on Neuron Auctions
Neuron Auctions auction continuous neuron intervention budgets on brand-specific orthogonal subspaces in LLMs to achieve strategy-proof revenue optimization while penalizing user utility loss.
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Semantic Needles in Document Haystacks: Sensitivity Testing of LLM-as-a-Judge Similarity Scoring
LLMs exhibit positional bias and context-dependent scoring patterns when judging document similarity, with each model showing a stable scoring fingerprint but a shared hierarchy of sensitivity to different semantic perturbations.
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RUBEN: Rule-Based Explanations for Retrieval-Augmented LLM Systems
RUBEN discovers minimal rule sets explaining RAG LLM outputs via novel pruning and applies them to evaluate LLM safety against adversarial injections.
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When Helpfulness Becomes Sycophancy: Sycophancy is a Boundary Failure Between Social Alignment and Epistemic Integrity in Large Language Models
Sycophancy is a boundary failure between social alignment and epistemic integrity, captured by a three-condition framework plus taxonomy of targets, mechanisms, and severity.
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Exploiting LLM-as-a-Judge Disposition on Free Text Legal QA via Prompt Optimization
Automatic prompt optimization using lenient LLM judges improves performance and transferability in legal QA evaluations compared to human design or strict judges.
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A Graph-Enhanced Defense Framework for Explainable Fake News Detection with LLM
G-Defense builds claim-centered graphs from sub-claims, applies RAG for evidence and competing explanations, then uses graph inference to detect fake news veracity and generate intuitive explanation graphs, claiming SOTA results.
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Domain-Adapted Retrieval for In-Context Annotation of Pedagogical Dialogue Acts
Domain-adapted utterance-level retrieval raises Cohen's kappa for tutoring dialogue act annotation to 0.526-0.580 on TalkMoves and 0.659-0.743 on Eedi, beating no-retrieval baselines by large margins across three LLMs.