Papers Semantic Retrieval
“Semantic Retrieval” 태그가 달린 논문 233편 · 필터 해제
Quantifying Organizational Environmental Action from Web Data and Large Language Models
Quantifying organizational environmental action from publicly available web content remains a challenging environmental data science problem because relevant information can be dispersed across multiple webpages and is p…
Semantic RetrievalIndustrial-Instruction: An End-to-End Framework for Building Instruction-Tuning and Benchmark Datasets from Industrial Technical Reports
Industrial technical reports contain high-value knowledge for maintenance, troubleshooting, and product engineering, but their heterogeneous structure (dense prose, specifications, tables) makes them difficult to index a…
Semantic RetrievalGeneral KnowledgeThe Retriever Should Remember: Experience-Amortized Reranking for Long-Term Agent Memory
Long-term language-model agents accumulate memories across interactions, but their retrievers typically do not accumulate retrieval experience. Semantic retrieval is efficient, but embedding similarity does not always re…
Semantic RetrievalFinRCA-Bench: Benchmarking Evidence Retrieval and Reasoning for Financial AI Systems
Large language models are increasingly used to support financial operations, but their apparent reasoning performance can depend on whether they receive the right evidence. In financial reconciliation, the evidence neede…
Semantic RetrievalCoAL-RAG: A Complexity-Aware Legal Retrieval-Augmented Generation Method
Legal consultation questions exhibit multi-level complexity. A single retrieval strategy often leads to over-reasoning for simple questions and poor interpretability for complex ones, making it difficult to meet the requ…
Semantic RetrievalLogical ReasoningINSPIRE: A Benchmark for Instruction-Aware Speech Retrieval
Existing speech retrieval systems rely on fixed similarity matching and cannot adapt to diverse user intents. We introduce INSPIRE, the first benchmark for instruction-aware speech retrieval, in which natural-language in…
Semantic RetrievalTRACE-Memory: Public-Conditioned Retrieval and Utility-Aware Evidence Admission for Personalized Generation
Personalized generation systems retrieve user history by request--memory relevance and inject it into the model context. Yet relevant history may concern the wrong preference aspect, duplicate public information, or prov…
Semantic RetrievalBeyond Initialization Loss: A Systematic Study of Token Embedding Initialization Strategies for LLM Vocabulary Extension
Vocabulary extension is an efficient way to adapt pretrained large language models (LLMs) to new languages, but the initialization of newly added token embeddings can strongly affect continued pre-training (CPT) efficien…
Semantic RetrievalFATE: Frame-Level Audio-Visual Temporal Embedding
When a dog opens its mouth and barks, humans naturally recognize what the sound is and when it occurs. Building audio-visual models with this same ability requires representations that capture both semantic and temporal …
Contrastive LearningSemantic RetrievalChronoMem: Version Control and Semantic Rollback for Large Language Model Agent Memory
LLM agents increasingly rely on long-term memory to support multi-session interaction and personalization. However, existing agent memory systems are designed around forward-only evolution, continuously accumulating, con…
Semantic RetrievalQuestion AnsweringA Graph-Native Bitemporal Memory Store for Conversational AI Agents
Conversational AI agents commonly lack persistent memory across sessions. The obvious fixes like injecting full chat histories into the context window, or delegating to a third-party memory service, either exhaust the mo…
Semantic RetrievalHarnessing LLMs for Reliable Academic Supervision: A Comparative Study
Large language models routinely produce fluent answers to single-shot prompts, yet deploying them as reliable components of a domain decision system is substantially harder. Closing this gap is the work of harness engine…
Semantic RetrievalProjAgent: Procedural Similarity Retrieval for Repository-Level Code Generation
Repository-level code generation requires implementing target functions while accounting for complex cross-file dependencies and project-specific conventions. Existing retrieval methods predominantly rely on lexical, str…
Semantic SimilaritySemantic RetrievalCode GenerationInject or Navigate? Token-Efficient Retrieval for LLM Analysis of Transactional Legal Documents
Answering questions over a set of transactional legal documents is most simply done by injecting the whole corpus into the LLM's context window on every query. That baseline maximises retrieval recall, but its token foot…
Semantic RetrievalNext-Gen Sponsored Search: Crafting the Perfect Query with Inventory-Aware RAG (InvAwr-RAG) Based GenAI
Sponsored search plays a crucial role in e-commerce revenue generation, where advertisers strategically bid on keywords to capture the attention of users through relevant search queries. However, the process of identifyi…
Semantic RetrievalWhen Classic Cache Policies Fail: Learning-Augmented Replacement for Semantic Retrieval Buffers
LLM agents increasingly rely on retrieval buffers to store and reuse past experience, yet the cache management policies governing these buffers remain largely ad-hoc. We formalize this as an online semantic cache replace…
Semantic RetrievalTowards Developing a Multimodal Chat Assistant for University Stakeholders: RAG-based Approach
University stakeholders often face difficulties in accessing timely and reliable information, especially in developing countries, where there are very few intelligent support systems. Existing rule-based chatbots are una…
Semantic RetrievalARMOR: Adaptive Retriever Optimization for Low-Resource Telecom Question Answering
Telecom question answering (QA) is a challenging setting for retrieval-augmented generation (RAG): evidence is fragmented across standards, papers, encyclopedic resources, and web documents, and answers often hinge on te…
Semantic RetrievalQuestion AnsweringAnswer GenerationHow Surprising Is Historical Italian to Language Models? Tokenization Tax, Comprehension Tax, and a Simple Mitigation
Large language models (LLMs) are increasingly critical to digital library workflows, yet their ability to process historical language remains poorly understood. Historical difficulty is typically treated as a monolithic …
Semantic RetrievalSchema-First Retrieval: Embedding Catalogs for Natural Language Analytics
Enterprise text-to-SQL systems often fail before SQL is generated: the model receives the wrong schema context. Modern warehouses contain thousands of tables, abbreviated columns, informal metrics, hidden join convention…
Semantic Retrieval