Language Modeling with Editable External Knowledge

• URL: http://arxiv.org/abs/2406.11830v1
• Date: Mon, 17 Jun 2024 17:59:35 GMT
• Title: Language Modeling with Editable External Knowledge
• Authors: Belinda Z. Li, Emmy Liu, Alexis Ross, Abbas Zeitoun, Graham Neubig, Jacob Andreas,
• Abstract summary: This paper introduces ERASE, which improves model behavior when new documents are acquired. It incrementally deletes or rewriting other entries in the knowledge base each time a document is added. It improves accuracy relative to conventional retrieval-augmented generation by 7-13% (Mixtral-8x7B) and 6-10% (Llama-3-8B) absolute.
• Score: 90.7714362827356
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: When the world changes, so does the text that humans write about it. How do we build language models that can be easily updated to reflect these changes? One popular approach is retrieval-augmented generation, in which new documents are inserted into a knowledge base and retrieved during prediction for downstream tasks. Most prior work on these systems have focused on improving behavior during prediction through better retrieval or reasoning. This paper introduces ERASE, which instead improves model behavior when new documents are acquired, by incrementally deleting or rewriting other entries in the knowledge base each time a document is added. In two new benchmark datasets evaluating models' ability to answer questions about a stream of news articles or conversations, ERASE improves accuracy relative to conventional retrieval-augmented generation by 7-13% (Mixtral-8x7B) and 6-10% (Llama-3-8B) absolute. Code and data are available at https://github.com/belindal/ERASE

Related papers

• Less is More: Making Smaller Language Models Competent Subgraph Retrievers for Multi-hop KGQA [51.3033125256716]
  We model the subgraph retrieval task as a conditional generation task handled by small language models. Our base generative subgraph retrieval model, consisting of only 220M parameters, competitive retrieval performance compared to state-of-the-art models. Our largest 3B model, when plugged with an LLM reader, sets new SOTA end-to-end performance on both the WebQSP and CWQ benchmarks.
  arXiv  Detail & Related papers  (2024-10-08T15:22:36Z)
• IncDSI: Incrementally Updatable Document Retrieval [35.5697863674097]
  IncDSI is a method to add documents in real time without retraining the model on the entire dataset. We formulate the addition of documents as a constrained optimization problem that makes minimal changes to the network parameters. Our approach is competitive with re-training the model on the whole dataset.
  arXiv  Detail & Related papers  (2023-07-19T07:20:30Z)
• BRENT: Bidirectional Retrieval Enhanced Norwegian Transformer [1.911678487931003]
  Retrieval-based language models are increasingly employed in question-answering tasks. We develop the first Norwegian retrieval-based model by adapting the REALM framework. We show that this type of training improves the reader's performance on extractive question-answering.
  arXiv  Detail & Related papers  (2023-04-19T13:40:47Z)
• DSI++: Updating Transformer Memory with New Documents [95.70264288158766]
  We introduce DSI++, a continual learning challenge for DSI to incrementally index new documents. We show that continual indexing of new documents leads to considerable forgetting of previously indexed documents. We introduce a generative memory to sample pseudo-queries for documents and supplement them during continual indexing to prevent forgetting for the retrieval task.
  arXiv  Detail & Related papers  (2022-12-19T18:59:34Z)
• Generate rather than Retrieve: Large Language Models are Strong Context Generators [74.87021992611672]
  We present a novel perspective for solving knowledge-intensive tasks by replacing document retrievers with large language model generators. We call our method generate-then-read (GenRead), which first prompts a large language model to generate contextutal documents based on a given question, and then reads the generated documents to produce the final answer.
  arXiv  Detail & Related papers  (2022-09-21T01:30:59Z)
• Autoregressive Search Engines: Generating Substrings as Document Identifiers [53.0729058170278]
  Autoregressive language models are emerging as the de-facto standard for generating answers. Previous work has explored ways to partition the search space into hierarchical structures. In this work we propose an alternative that doesn't force any structure in the search space: using all ngrams in a passage as its possible identifiers.
  arXiv  Detail & Related papers  (2022-04-22T10:45:01Z)
• Editing Factual Knowledge in Language Models [51.947280241185]
  We present KnowledgeEditor, a method that can be used to edit this knowledge. Besides being computationally efficient, KnowledgeEditor does not require any modifications in LM pre-training. We show KnowledgeEditor's efficacy with two popular architectures and knowledge-intensive tasks.
  arXiv  Detail & Related papers  (2021-04-16T15:24:42Z)

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.