Context-Efficient Retrieval with Factual Decomposition
There has recently been considerable interest in incorporating information retrieval into large language models (LLMs). Retrieval from a dynamically expanding external corpus of text allows a model to incorporate current events and can be viewed as a form of episodic memory. Here we demonstrate that pre-processing the external corpus into semi-structured ''atomic facts'' makes retrieval more efficient. More specifically, we demonstrate that our particular form of atomic facts improves performance on various question answering tasks when the amount of retrieved text is limited. Limiting the amount of retrieval reduces the size of the context and improves inference efficiency.
Code (0)
등록된 구현이 없습니다.
Tasks
FormInformation RetrievalQuestion AnsweringRetrievalSimilar Papers 제목 키워드 기반
Enhancing Post-Hoc Attributions in Long Document Comprehension via Coarse Grained Answer Decomposition
Accurately attributing answer text to its source document is crucial for developing a reliable question-answering system. However, attribution for long documents remains largely unexplored. Post-hoc attribution systems a…
In-Context LearningQuestion AnsweringDnDScore: Decontextualization and Decomposition for Factuality Verification in Long-Form Text Generation
The decompose-then-verify strategy for verification of Large Language Model (LLM) generations decomposes claims that are then independently verified. Decontextualization augments text (claims) to ensure it can be verifie…
FormLanguage ModelingLanguage ModellingLarge Language Model+1Causal-Counterfactual RAG: The Integration of Causal-Counterfactual Reasoning into RAG
Large language models (LLMs) have transformed natural language processing (NLP), enabling diverse applications by integrating large-scale pre-trained knowledge. However, their static knowledge limits dynamic reasoning ov…
Semantic SimilarityAgent Retrieval Bench: Evaluating Repository Context Retrieval for Coding Agents
Modern coding agents are usually evaluated by whether they eventually produce a correct patch, but patch generation depends on an earlier context-acquisition stage: finding the repository files needed for the task. We in…
Semantic SimilarityMemMachine: A Ground-Truth-Preserving Memory System for Personalized AI Agents
Large Language Model (LLM) agents require persistent memory to maintain personalization, factual continuity, and long-horizon reasoning, yet standard context-window and retrieval-augmented generation (RAG) pipelines degr…