DynamicER: Resolving Emerging Mentions to Dynamic Entities for RAG

• URL: http://arxiv.org/abs/2410.11494v1
• Date: Tue, 15 Oct 2024 10:57:12 GMT
• Title: DynamicER: Resolving Emerging Mentions to Dynamic Entities for RAG
• Authors: Jinyoung Kim, Dayoon Ko, Gunhee Kim,
• Abstract summary: We introduce a novel task aimed at resolving emerging mentions to dynamic entities. Our benchmark includes dynamic entity mention resolution and entity-centric knowledge-intensive QA task. We propose a temporal segmented clustering method with continual adaptation, effectively managing the temporal dynamics of evolving entities and emerging mentions.
• Score: 40.40642344216866
• License:
• Abstract: In the rapidly evolving landscape of language, resolving new linguistic expressions in continuously updating knowledge bases remains a formidable challenge. This challenge becomes critical in retrieval-augmented generation (RAG) with knowledge bases, as emerging expressions hinder the retrieval of relevant documents, leading to generator hallucinations. To address this issue, we introduce a novel task aimed at resolving emerging mentions to dynamic entities and present DynamicER benchmark. Our benchmark includes dynamic entity mention resolution and entity-centric knowledge-intensive QA task, evaluating entity linking and RAG model's adaptability to new expressions, respectively. We discovered that current entity linking models struggle to link these new expressions to entities. Therefore, we propose a temporal segmented clustering method with continual adaptation, effectively managing the temporal dynamics of evolving entities and emerging mentions. Extensive experiments demonstrate that our method outperforms existing baselines, enhancing RAG model performance on QA task with resolved mentions.

Related papers

• Retriever-and-Memory: Towards Adaptive Note-Enhanced Retrieval-Augmented Generation [72.70046559930555]
  We propose a generic RAG approach called Adaptive Note-Enhanced RAG (Adaptive-Note) for complex QA tasks. Specifically, Adaptive-Note introduces an overarching view of knowledge growth, iteratively gathering new information in the form of notes. In addition, we employ an adaptive, note-based stop-exploration strategy to decide "what to retrieve and when to stop" to encourage sufficient knowledge exploration.
  arXiv  Detail & Related papers  (2024-10-11T14:03:29Z)
• KaPQA: Knowledge-Augmented Product Question-Answering [59.096607961704656]
  We introduce two product question-answering (QA) datasets focused on Adobe Acrobat and Photoshop products. We also propose a novel knowledge-driven RAG-QA framework to enhance the performance of the models in the product QA task.
  arXiv  Detail & Related papers  (2024-07-22T22:14:56Z)
• Enhancing Retrieval-Augmented LMs with a Two-stage Consistency Learning Compressor [4.35807211471107]
  This work proposes a novel two-stage consistency learning approach for retrieved information compression in retrieval-augmented language models. The proposed method is empirically validated across multiple datasets, demonstrating notable enhancements in precision and efficiency for question-answering tasks.
  arXiv  Detail & Related papers  (2024-06-04T12:43:23Z)
• DVIS-DAQ: Improving Video Segmentation via Dynamic Anchor Queries [60.09774333024783]
  We introduce Dynamic Anchor Queries (DAQ) to shorten the transition gap between the anchor and target queries. We also introduce a query-level object Emergence and Disappearance Simulation (EDS) strategy, which unleashes DAQ's potential without any additional cost. Experiments demonstrate that DVIS-DAQ achieves a new state-of-the-art (SOTA) performance on five mainstream video segmentation benchmarks.
  arXiv  Detail & Related papers  (2024-03-29T17:58:50Z)
• Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy [164.83371924650294]
  We show that strong performance can be achieved by a method we call Iter-RetGen, which synergizes retrieval and generation in an iterative manner. A model output shows what might be needed to finish a task, and thus provides an informative context for retrieving more relevant knowledge. Iter-RetGen processes all retrieved knowledge as a whole and largely preserves the flexibility in generation without structural constraints.
  arXiv  Detail & Related papers  (2023-05-24T16:17:36Z)
• Retrieval-guided Counterfactual Generation for QA [5.434621727606356]
  We focus on the task of creating counterfactuals for question answering. We develop a Retrieve-Generate-Filter technique to create counterfactual evaluation and training data. We find that RGF data leads to significant improvements in a model's robustness to local perturbations.
  arXiv  Detail & Related papers  (2021-10-14T17:56:37Z)
• GQE-PRF: Generative Query Expansion with Pseudo-Relevance Feedback [8.142861977776256]
  We propose a novel approach which effectively integrates text generation models into PRF-based query expansion. Our approach generates augmented query terms via neural text generation models conditioned on both the initial query and pseudo-relevance feedback. We evaluate the performance of our approach on information retrieval tasks using two benchmark datasets.
  arXiv  Detail & Related papers  (2021-08-13T01:09:02Z)
• Probing Linguistic Features of Sentence-Level Representations in Neural Relation Extraction [80.38130122127882]
  We introduce 14 probing tasks targeting linguistic properties relevant to neural relation extraction (RE) We use them to study representations learned by more than 40 different encoder architecture and linguistic feature combinations trained on two datasets. We find that the bias induced by the architecture and the inclusion of linguistic features are clearly expressed in the probing task performance.
  arXiv  Detail & Related papers  (2020-04-17T09:17:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.