arXiv Computation and Language
RCEM: Robust Conversational Search EMbedder in Distributional Shift
Read the original on arXiv Computation and Language →The Flow has not summarised this story yet — read it at arXiv Computation and Language.
Related stories
arXiv AI
Sep 15
CompCQR: Compositional Query Generation for Training-Free Conversational Search
arXiv:2609.14646v1 Announce Type: cross Abstract: Multi-turn interactions with LLMs are becoming increasingly common in information-seeking scenarios. However, user queries are often ambiguous and co...
arXiv Computation and Language
Sep 15
Where to Look and What to Use: Retrieve-Localize-Generate for Long-Term Conversational Memory Question Answering
arXiv:2609.07093v2 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) enables large language models (LLMs) to answer questions by accessing external knowledge and has been widely a...
arXiv Machine Learning
Jun 15
Efficient Rationale-based Retrieval: On-policy Distillation from Generative Rerankers based on JEPA
arXiv:2604. 23336v3 Announce Type: replace-cross Abstract: Unlike traditional fact-based retrieval, rationale-based retrieval typically necessitates cross-encoding of query-document pairs using large language models, incurring substantial computational costs.
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 Computation and Language
Aug 31
Entity-Memory Graph Retrieval Improves Evidence Coverage in Long-Conversation Question Answering
Entity-Memory graph retrieval preserves dialogue turns as verbatim memory nodes, links repeated mentions via shared entities, and connects adjacent memories with chronological edges. During retrieval, the system gates through entities, fuses semantics, and performs one‑hop chronological recovery before dense backfill, allowing it to keep neighboring memories that dense cosine ranking might miss. On 1,986 questions from ten LoCoMo conversations, this graph retrieval method increases official evidence recall at top‑k 25 from 79.7468 % to 84.4842 %, with the advantage extending from top‑k 5 to 50, though it does not improve overall final‑answer F1.
arXiv Computation and Language
Oct 12
Beyond the Final Embedding: Retrieving Memories from Token-Level Evidence
The paper introduces a memory-augmented retrieval method that leverages token-level evidence instead of relying solely on single-vector embeddings. By performing sparse token matching and aggregating scores with whole-memory similarity, the approach improves retrieval performance across eight benchmarks and various model families without extra training. An optional low-rank query adapter can further boost results when supervised.