arXiv:2606. 29947v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as rerankers in recommender systems, with the expectation that semantic understanding will help in cold-start and long-tail regimes.
The paper proposes an incremental pooled LLM evaluation method for selecting retrieval models in production Retrieval-Augmented Generation (RAG) systems. By having a language model judge the union of documents retrieved by current candidates and expanding the pool only with new documents from added systems, the approach reuses judgments across all systems. Experiments on four benchmarks and a financial news QA deployment show strong correlation with gold-standard rankings, high preservation of pairwise orderings, and significant cost savings—up to 4.9× lower evaluation cost and 65–80% judgment reuse.
arXiv:2608. 10528v1 Announce Type: cross Abstract: Anchor-based pointwise LLM reranking scores each candidate against a shared reference passage to recover cross-document context at pointwise cost.
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: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.
The study evaluates large language models (LLMs) as rerankers in conversational movie recommendation, comparing proprietary, open-weight, and fine-tuned LLMs against collaborative-filtering and sequential baselines on the ReDial benchmark. Results show that the best proprietary LLM achieves an NDCG@10 of 0.1497 with a shared semantic candidate pool, outperforming non-LLM baselines, while open-weight LLMs do not surpass a tuned shallow autoencoder under the same protocol. The analysis also highlights that reranker performance is highly sensitive to candidate generation, pool size, scoring policy, and decoding temperature, suggesting these factors should be reported as standard evaluation fields.
arXiv:2608.22859v1 Announce Type: cross Abstract: RAG systems are increasingly used to summarize what large collections of documents say. A user asks "What do people think about X?" and receives an a...
arXiv:2608. 02112v1 Announce Type: new Abstract: Embedding benchmarks measure standalone model quality, but they do not establish whether a low-cost retriever contributes complementary ranking information once lexical and transformer-based retrieval are already combined.
arXiv:2510. 06732v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used as rerankers in information retrieval, yet their ranking behavior can be steered by small, natural-sounding prompts.
arXiv:2608. 08994v1 Announce Type: cross Abstract: Retrieving relevant evidence from noisy web data is challenging, particularly in sensitive domains containing incomplete reports, heterogeneous language, and irrelevant content.
Anchor-based pointwise LLM reranking scores each candidate against a shared reference passage to recover cross-document context at pointwise cost. We study when this actually helps, using GCCP/PAGC as a representative method.
arXiv:2604. 19047v2 Announce Type: replace-cross Abstract: Existing QA benchmarks typically assume distinct documents with minimal overlap, yet real-world retrieval-augmented generation (RAG) systems operate on corpora such as financial reports, legal codes, and patents, where information is highly redundant and documents exhibit strong inter-document similarity.