Presents cue interventions and tie-aware metrics to detect rationalization bias in LLM judges and demonstrates that PROOF-BEFORE-PREFERENCE reduces cue anchoring compared to baselines.
Evalassist: Llm-as-a-judge simplified,
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
SafeMoE isolates unsafe knowledge in domain-specific LoRA experts and routes them via a lightweight gate trained on safe responses to produce safer and more informative LLM outputs with zero-shot generalization.
BADGER is a new enterprise evaluation framework that adds LLM-assisted SQL component extraction and a Hybrid-EX metric validated on 150 human-annotated queries to existing text-to-SQL and agentic assessment methods.
citing papers explorer
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Faithful or Fabricated? A Causal Framework for Rationalization Bias in LLM Judges
Presents cue interventions and tie-aware metrics to detect rationalization bias in LLM judges and demonstrates that PROOF-BEFORE-PREFERENCE reduces cue anchoring compared to baselines.
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Dialectics of Alignment: Harnessing Unsafe Knowledge for Dynamic Safety Routing
SafeMoE isolates unsafe knowledge in domain-specific LoRA experts and routes them via a lightweight gate trained on safe responses to produce safer and more informative LLM outputs with zero-shot generalization.
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BADGER: Bridging Agentic and Deterministic Evaluation for Generative Enterprise Reasoning
BADGER is a new enterprise evaluation framework that adds LLM-assisted SQL component extraction and a Hybrid-EX metric validated on 150 human-annotated queries to existing text-to-SQL and agentic assessment methods.