arXiv Computation and Language
TF-IDF and BM25 Are Exact KL Divergences
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Jun 16
Entity Labels Are Not Entity Signals: A Framework for Observable Relevance in Document Re-Ranking
arXiv:2606. 15998v1 Announce Type: cross Abstract: Entity-aware document retrieval uses query-associated entities as ranking signals, assuming that semantically relevant entities are also useful retrieval signals.
arXiv AI
Jun 3
Do Neural Retrievers Prefer Certain Documents? Evidence of Learned Relevance Priors
arXiv:2606. 02814v1 Announce Type: cross Abstract: Neural retrievers are trained to estimate query-document relevance from annotated query-document pairs.
arXiv AI
Jun 30
Beyond the Reranker: Do RAG Retrieval Enhancements Help Once a Strong Reranker Is Present?
arXiv:2606. 28367v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) is routinely extended with methods meant to improve retrieval: query expansion, hierarchical and cross-document summarization, graph-based expansion, per-query routing, rank fusion, and corrective re-retrieval.
arXiv Computation and Language
Aug 25
Robustness of IR Models to Collection Growth
The paper investigates how adding non‑relevant documents to a collection affects the performance of Information Retrieval (IR) models. It distinguishes between Multi‑Document‑Agnostic (MDA) models, which do not rely on other documents, and Multi‑Document‑Dependent (MDD) models, which do, and evaluates both on two collections with little topic overlap. Results show that all models suffer some performance loss when non‑relevant documents are added, but MDA models outperform MDD models in retrieval tasks, while both perform similarly in reranking.
arXiv AI
Jul 24
A Comparative Evaluation of Embeddings and LLMs in a Greek Book Publisher Setting - The CUP Dataset
arXiv:2607. 21274v1 Announce Type: cross Abstract: We present CUP, a Greek book retrieval benchmark consisting of 868 catalog records and 104 expert-annotated queries with graded relevance judgments.
arXiv AI
Aug 25
From Association to Causation: Improving Retrieval Precision of Retrieval-Augmented Generation via Causal Relations and an Attention Mechanism
arXiv:2608.21702v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) grounds LLM generation on retrieved documents, but the standard terminal retrieval stage--dense-vector similarity,...