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cs.IR2026
Rubric-Calibrated Preferences: Cross-Query Calibration of LLM Judgments via Item Response Theory
Fabian David Schmidt, Donato Crisostomi, Carlos Lassance +1
Rerankers decide which documents users and LLMs see, yet their standard metric, nDCG, relies on human relevance labels that are costly, sparse, noisy, and discretely graded. As rer…
cs.CL2026
LLM2Vec-Gen: Generative Embeddings from Large Language Models
Parishad BehnamGhader, Vaibhav Adlakha, Fabian David Schmidt +3
Fine-tuning LLM-based text embedders via contrastive learning maps inputs and outputs into a new representational space, discarding the LLM's output semantics. We propose LLM2Vec-G…