Retrieving a Set, Not Independent Passages: Set-Level Compatibility Learning for Efficient Set Exploration

QUBO-Optimized Evidence Selection for Retrieval-Augmented Question Answering with Unconventional Solvers

The QUBO selector achieves competitive exact-match and token-F1 performance relative to LLM-based selectors while providing a solver-compatible formulation for structured evidence selection, suggesting that multi-hop evidence selection can be cast as discrete optimization, opening a path toward RAG pipelines where LLMs are reserved for semantic processing and answer generation, while context selection is handled by Ising/QUBO-compatible solvers.

Rank-ICL: Ranking-based In-context Learning for Search Result Explanation

Evaluation on WikiSA and ExaRank shows that ranking-based few-shot prompting generally improves over zero-shot prompting and achieves competitive performance against random-shot prompting, indicating that retrieval-based demonstration selection is beneficial but not uniformly superior in all settings.

Optimized Hybrid Retrieval-Augmented Generation Framework using Semantic Search and Re-ranking for Reliable LLM Responses

Building question-answering systems that can read a document and answer naturally phrased questions about it is difficult when retrieval is left to either keyword matching or dense vector search alone, since each method has blind spots that surface as missed context, near-miss answers, or content invented by the underlying language model. This paper describes an optimized hybrid Retrieval-Augmented Generation (RAG) pipeline built to reduce these failure modes by combining two complementary retrieval signals: dense semantic similarity computed over a FAISS vector index, and sparse lexical scoring computed with BM25. Candidates returned by both retrievers are merged and passed through a Cross-Encoder re-ranking stage that scores each query-passage pair jointly, pushing the most contextually relevant chunks to the top before they reach the language model. Final answers are produced by Google’s Gemini model under a prompt that restricts it to the supplied context, which keeps the output tied to the source document rather than to whatever the model already “knows.” The pipeline is exposed through a Streamlit application that lets a user upload a PDF and ask questions about it in plain language, returning each answer alongside a confidence estimate and the page it came from. Evaluation on a multi-page technical PDF document shows that the hybrid retrieval and re-ranking stages together raise retrieval precision and reduce irrelevant or unsupported answers compared with retrieval limited to a single method, supporting the use of this approach for reliable, document-grounded question answering.

Candidate-Constrained Retrieval-Augmented Generation for LongEval-RAG: System Design and Empirical Analysis

We present a candidate-constrained retrieval-augmented generation system for LongEval-RAG, where each query is associated with an organizer-provided candidate set and all retrieved evidence and final citations must remain within that set. The system combines deterministic provenance tracking with passage-based retrieval, deterministic query expansion, pseudo-relevance feedback (PRF), reciprocal rank fusion (RRF), lightweight evidence reranking, citation-aware evidence aggregation, and optional MiniLM sentence reranking. We evaluate ten pipeline variants using a primary organizer evaluation and a supplementary self-generated diagnostic protocol. The primary evaluation shows that the strongest balanced variant is rule-minilm: a rule-based chunking pipeline with query expansion, PRF, RRF, reranking, citation prior, and late MiniLM sentence selection. This variant obtains the highest BERTScore, retrieval precision, nugget coverage, and average grade among our submissions. The result suggests that the main gain does not come from more complex semantic or topic-shift chunking, but from pairing stable rule-based evidence units with sentence-level neural selection before generation. The supplementary LLM-judge evaluation remains useful for early diagnosis and additional analysis, but it emphasizes different systems than the primary gold-answer and nugget-based evaluation, highlighting the need for multi-metric RAG evaluation.

Efficient and reliable multimodal retrieval-augmented question answering via greedy leave-one-out consistency pruning

Select, Don't Train: The Benefits of Modular Entity Disambiguation with LLM-Based Selection

Entity Disambiguation (ED) is a key task for constructing and using knowledge graphs. State-of-the-art neural approaches commonly model ED as a single task, although it consists of two distinct subproblems: retrieving candidate entities and selecting the correct one given context. Dual-encoder models optimize for both within a shared embedding space, forcing representations to balance high-recall retrieval with fine-grained selection, and they require trained retrievers, which are costly to maintain as knowledge graphs change. While recent work has begun to combine retrievers with LLM-based selectors, the interplay between the two stages has not been studied systematically. In this paper, we present a systematic comparison of retrieval strategies for candidate generation under a shared LLM-based selection stage, combining sparse retrieval (BM25), Web KB search, and a state-of-the-art trained dense retriever with several open- and closed-source LLMs. We show that, once selection is delegated to a capable LLM, training the retriever provides only modest additional value: a fully training-free BM25 retriever paired with an LLM selector reaches a new state of the art on the ZELDA benchmark, raising inKB micro-F1 from 82.3 to 86.3 (+4); pairing the same LLM with a trained dense retriever reaches 88.5. Decoupling retrieval from selection also exposes a limitation of current ED systems: when the correct entity is missing from retrieved candidates, they are forced to predict an incorrect entity. In contrast, our framework allows for abstention when retrieval failure is detected. In an evaluation setting that rewards correct abstentions, the training-free BM25 + LLM pipeline reaches 90.7 F1.