CeQe: Grounding Lexical Retrieval in Semantic Evidence

Leveraging Language Models for Textual Retrieval in the TREC 2025 DRAGUN Track

Conversational Query Reformulation Using Fine-Grained Retrieval and Keyword Augmentation

Conversational Query Reformulation (CQR) is an important component in Conversational Question Answering (ConvQA), where user queries are often incomplete, ambiguous, and dependent on previous dialogue turns. Recent CQR approaches have shown the effectiveness of large language models (LLMs) in generating standalone queries. However, rewritten queries do not always lead to optimal retrieval performance. This paper presents a retrieval-aware CQR framework that integrates fine-grained retrieval feedback and keyword augmentation to improve downstream document retrieval. Instead of treating the LLM-rewritten query as the final search query, our framework uses it to retrieve candidate documents, identify relevant paragraph-level evidence, generate a pseudo-answer, and extract semantically relevant keywords. These keywords are then filtered based on their alignment with the reformulated query and dialogue history before being incorporated into the final query. We evaluate the proposed framework on a sampled subset of the QReCC dataset using BM25 and GTR as sparse and dense retrieval backbones. The experimental results show that our proposed approach improves retrieval accuracy compared to the baseline model across all metrics such as Recall@10, MRR and NDCG. Specifically, under the BM25 sparse retrieval framework, our best-performing configuration achieves improvements in Recall@10 (81.29), MRR (56.06) and NDCG (60.92) compared to the baseline. A comparable pattern of substantial gains holds under the GTR dense retrieval framework, achieving a Recall@10 (76.51), MRR (53.26) and NDCG (57.54), indicating the effectiveness of our method across different retrieval paradigms.

Sequran: Composite Scoring and Reranking Techniques for Refining Quran Search Engine Results

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

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.

Difficulty-Gated Fusion of Reasoning Views for Temporal Retrieval

Reasoning-intensive temporal retrieval requires matching a query to documents whose relevance depends on shared temporal reasoning rather than lexical overlap. Expanding a query into several reformulations that make its temporal intent explicit, and retrieving with each, supplies this reasoning, but fusing the resulting rankings with equal weights wastes accuracy: for any single query, only some reformulations are reliable. We propose query-difficulty-gated fusion of reasoning views. From each view we read an eight-dimensional signature of its score distribution, built from query-performance-prediction quantities such as softmax entropy, score gaps, and dispersion, and a gate of roughly one thousand parameters maps these signatures to per-query view weights. The fused ranking uses no relevance labels at inference, no re-ranking, and no fine-tuning of the retriever; the gate is trained leave-one-task-out. On the \textsc{Tempo} benchmark, the method improves all six retrievers we evaluate, from BERT encoders to 7B decoder retrievers, with the largest gains on the weaker backbones. The strongest retrievers reach $0.297$ and $0.303$ nDCG@10, and the per-query gain over the original query is significant under a paired bootstrap ($p<0.001$). A per-query oracle reaches $0.364$ against our realized $0.297$, exposing headroom that identifies per-query view selection as a concrete next step.

Factorized Hypothesis Search for Evidence-to-Taxonomy Retrieval

Large-taxonomy retrieval often assumes that the input already expresses the target concept. In many settings, however, the input is indirect evidence, such as a table cell whose meaning depends on its row, column, datatype, and context. We call this mismatch the retrieval readiness gap. Our analysis shows that the current index retrieves the target reliably when its semantics are explicit, while raw evidence often leaves it deep in the ranking. We propose Factorized Hypothesis Search (FHS), which maintains multiple partial interpretations over named semantic dimensions. These hypotheses support structured query rendering, multi-hypothesis retrieval, and dimension-level candidate verification. On both financial taxonomy tagging and CodiEsp clinical coding tasks, FHS achieves the best Recall@1, MRR, and final accuracy among the non-oracle methods. Replacing the factorized hypothesis path with a free-text ensemble causes the largest drop in head-ranking performance, while sequential refinement provides no additional gain over FHS's strong parallel first round.