REVIEW 10 cited by
UniMS-RAG: A Unified Multi-source Retrieval-Augmented Generation for Personalized Dialogue Systems
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
Large Language Models (LLMs) has shown exceptional capabilities in many natual language understanding and generation tasks. However, the personalization issue still remains a much-coveted property, especially when it comes to the multiple sources involved in the dialogue system. To better plan and incorporate the use of multiple sources in generating personalized response, we firstly decompose it into three sub-tasks: Knowledge Source Selection, Knowledge Retrieval, and Response Generation. We then propose a novel Unified Multi-Source Retrieval-Augmented Generation system (UniMS-RAG) Specifically, we unify these three sub-tasks with different formulations into the same sequence-to-sequence paradigm during the training, to adaptively retrieve evidences and evaluate the relevance on-demand using special tokens, called acting tokens and evaluation tokens. Enabling language models to generate acting tokens facilitates interaction with various knowledge sources, allowing them to adapt their behavior to diverse task requirements. Meanwhile, evaluation tokens gauge the relevance score between the dialogue context and the retrieved evidence. In addition, we carefully design a self-refinement mechanism to iteratively refine the generated response considering 1) the consistency scores between the generated response and retrieved evidence; and 2) the relevance scores. Experiments on two personalized datasets (DuLeMon and KBP) show that UniMS-RAG achieves state-of-the-art performance on the knowledge source selection and response generation task with itself as a retriever in a unified manner. Extensive analyses and discussions are provided for shedding some new perspectives for personalized dialogue systems.
Forward citations
Cited by 10 Pith papers
-
Constructing and Evaluating Declarative RAG Pipelines in PyTerrier
PyTerrier-RAG extends PyTerrier with datatypes, readers, datasets, and metrics for building and evaluating declarative retrieval-augmented generation pipelines.
-
PersonaLens: A Benchmark for Personalization Evaluation in Conversational AI Assistants
PersonaLens uses LLM-simulated users and an LLM judge to evaluate personalization in task-oriented conversational assistants across 111 tasks and 20 domains.
-
RAG-WM: An Efficient Black-Box Watermarking Approach for Retrieval-Augmented Generation of Large Language Models
RAG-WM embeds HMAC-generated entity-relation watermarks into a RAG knowledge base and detects stolen RAGs via black-box queries with a binomial test, showing high success across four LLMs and five datasets.
-
On the Way to LLM Personalization: Learning to Remember User Conversations
Finetuning a LoRA adapter on self-generated question-answer pairs lets Llama 3 8B recall conversation topics with 81.5% accuracy, close to RAG at 83.5% but without retrieval.
-
Benchmarking and Learning Real-World Customer Service Dialogue
OlaMind, a Learn-to-Think plus basic-to-hard RL pipeline for RAG customer service, reports +28.92% issue resolution, -6.08% human transfer online, and an 8.6% offline hallucination rate.
-
Masking in Multi-hop QA: An Analysis of How Language Models Perform with Context Permutation
Ordering retrieved documents along the reasoning chain and replacing the causal mask with a prefix mask during LoRA fine-tuning improves multi-hop QA accuracy; peak attention scores can select the best context order.
-
Understanding the Fundamental Design Decisions of Retrieval-Augmented Generation Systems
RAG deployment must be context-aware: QA benefits from 5 to 10 retrieved documents, code generation has no stable optimal document count, and prompting helps code tasks far more than QA tasks.
-
GenFlow: Interactive Modular System for Image Generation
GenFlow combines a node-based editor, retrieval-augmented workflow search, and web-exploration agents to simplify Stable Diffusion image-generation workflows, with a small user study reporting reduced task times and p...
-
Federated Retrieval-Augmented Generation: A Systematic Mapping Study
A systematic mapping study that classifies 18 federated RAG papers into a taxonomy and highlights evaluation gaps, though its search protocol is not reproducible.
-
Text Data Augmentation for Large Language Models: A Comprehensive Survey of Methods, Challenges, and Opportunities
A literature review that classifies LLM text data augmentation into simple, prompt-based, retrieval-based, and hybrid techniques, with post-processing and evaluation notes.
Discussion (0). Continue with ORCID to comment.