InvariRank achieves permutation-invariant listwise reranking for LLM-based recommendations via a structured attention mask that blocks cross-candidate interactions and shared positional framing under RoPE, enabling stable rankings in one forward pass.
Evaluating position bias in large language model recommendations,
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3representative citing papers
A systematic review of over 200 studies concludes that LLMs in recommender systems act as a double-edged sword, creating both opportunities and new risks for trustworthiness.
Domain-specific fine-tuning of an LLM for NER-RE on human-smuggling court texts yields 15.5% and 31.46% absolute F1 gains over a larger baseline, with reduced noise, duplication, and runtime.
citing papers explorer
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One Pass, Any Order: Position-Invariant Listwise Reranking for LLM-Based Recommendation
InvariRank achieves permutation-invariant listwise reranking for LLM-based recommendations via a structured attention mask that blocks cross-candidate interactions and shared positional framing under RoPE, enabling stable rankings in one forward pass.
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Trustworthy Recommendation in the Era of Large Language Models: Opportunities and Challenges
A systematic review of over 200 studies concludes that LLMs in recommender systems act as a double-edged sword, creating both opportunities and new risks for trustworthiness.
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FineREX: Fine-Tuned NER-RE for Human Smuggling Knowledge Graphs
Domain-specific fine-tuning of an LLM for NER-RE on human-smuggling court texts yields 15.5% and 31.46% absolute F1 gains over a larger baseline, with reduced noise, duplication, and runtime.