UnIte selects target-domain documents for pseudo-query generation by filtering high aleatoric uncertainty and prioritizing high epistemic uncertainty, yielding +2.45 to +3.49 nDCG@10 gains on BEIR with ~4k samples.
Mitko Gospodinov, Sean MacAvaney, and Craig Mac- donald
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
DiffRetriever uses parallel masked tokens in diffusion LMs for retrieval representations, outperforming DiffEmbed and other baselines on aggregate effectiveness while supporting efficient multi-representation matching.
FaithMed applies reinforcement learning with process-level rewards derived from evidence-based medicine rubrics to improve both task performance and reasoning faithfulness in medical LLMs.
citing papers explorer
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UnIte: Uncertainty-based Iterative Document Sampling for Domain Adaptation in Information Retrieval
UnIte selects target-domain documents for pseudo-query generation by filtering high aleatoric uncertainty and prioritizing high epistemic uncertainty, yielding +2.45 to +3.49 nDCG@10 gains on BEIR with ~4k samples.
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DiffRetriever: Parallel Representative Tokens for Retrieval with Diffusion Language Models
DiffRetriever uses parallel masked tokens in diffusion LMs for retrieval representations, outperforming DiffEmbed and other baselines on aggregate effectiveness while supporting efficient multi-representation matching.
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FaithMed: Training LLMs For Faithful Evidence-Based Medical Reasoning
FaithMed applies reinforcement learning with process-level rewards derived from evidence-based medicine rubrics to improve both task performance and reasoning faithfulness in medical LLMs.