Hugging Face Trending Papers
Gap Detection: Mitigating Homogenization through Demand and Defensibility
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arXiv Machine Learning
Oct 15
Gap Detection: Mitigating Homogenization through Demand and Defensibility
arXiv:2610.20162v1 Announce Type: cross Abstract: Language models often produce semantically similar answers to open-ended questions, even when many plausible alternatives exist. Generating useful al...
arXiv Machine Learning
Jul 28
SMART: LLM-Augmented Hybrid Retrieval for Dynamic Product Ads
arXiv:2607. 23121v1 Announce Type: cross Abstract: Dynamic Product Ads (DPA) require retrieving relevant items from multi-million product catalogs, balancing two competing objectives: retargeting (re-surfacing known interests) and prospecting (discovering new categories).
arXiv AI
Sep 24
Query Implied Generative Engine Optimization
The paper "Query Implied Generative Engine Optimization" introduces QI‑GEO, a method that infers user intent directly from documents to enhance visibility in Generative Search Engines. By approximating a document’s intent space, QI‑GEO identifies missing yet relevant content, improving objective scores by up to 15.9% and subjective scores by up to 17.6% on GEO‑Bench datasets. The approach yields nearly twice as many citation gains as losses, demonstrating that document‑derived intent approximations can boost content visibility without explicit query inputs.
arXiv AI
Aug 19
DEPT: Document Embedding Preservation Tuning for Unified Query Expansion and Retrieval
The paper introduces DEPT, a method that trains a single decoder-only large language model to both expand queries and encode documents for retrieval. By preserving document embeddings close to their initial cached values while allowing gradients to flow through the generator, DEPT stabilizes retrieval targets and enables efficient index reuse and online hard‑negative mining. Experiments on the BEIR benchmark with Qwen3‑4B‑Instruct‑2507 and LLaMA‑3.2‑3B‑Instruct show that DEPT outperforms training‑free, independently trained, and staged unified baselines, with ablations confirming the benefits of preservation, whitening, end‑to‑end expansion training, and online negatives.
arXiv AI
1d ago
A Small Model Asks Better: Fine-Tuning Query Clarification for Retrieval-Augmented Generation
arXiv:2610.28686v1 Announce Type: new Abstract: Query clarification is essential for retrieval-augmented generation (RAG) systems that face ambiguous user queries. We consider multi-turn grounded con...
arXiv Computation and Language
Oct 12
Tracing and Taming Hallucinated Terms via Subsequence-Association Filtering and Breadth-Aware Selection for LLM Query Expansion
The paper introduces SAFE‑QE, a training‑free, retriever‑agnostic method for query expansion that mitigates hallucinated terms from large language models. It uses a subsequence‑association filter to score candidate terms based on their reproducibility across randomised query contexts, down‑weighting terms that lack corpus support. SAFE‑QE then selects a broad yet consistent set of expansions via constrained coverage maximisation, and defines a new metric, the Hallucinated Expansion Term Rate, to quantify faithfulness. The approach matches state‑of‑the‑art ranking performance while injecting far fewer unsupported terms on TREC and BEIR benchmarks.