MODOC: A Modular Interface for Flexible Interlinking of Text Retrieval and Text Generation Functions

• URL: http://arxiv.org/abs/2408.14623v1
• Date: Mon, 26 Aug 2024 20:36:52 GMT
• Title: MODOC: A Modular Interface for Flexible Interlinking of Text Retrieval and Text Generation Functions
• Authors: Yingqiang Gao, Jhony Prada, Nianlong Gu, Jessica Lam, Richard H. R. Hahnloser,
• Abstract summary: Large Language Models (LLMs) produce eloquent texts but often the content they generate needs to be verified. Traditional information retrieval systems can assist with this task, but most systems have not been designed with LLM-generated queries in mind. We present MODOC, a modular user interface that leverages the capabilities of LLMs and provides assistance with detecting their confabulations.
• Score: 8.624104798224085
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) produce eloquent texts but often the content they generate needs to be verified. Traditional information retrieval systems can assist with this task, but most systems have not been designed with LLM-generated queries in mind. As such, there is a compelling need for integrated systems that provide both retrieval and generation functionality within a single user interface. We present MODOC, a modular user interface that leverages the capabilities of LLMs and provides assistance with detecting their confabulations, promoting integrity in scientific writing. MODOC represents a significant step forward in scientific writing assistance. Its modular architecture supports flexible functions for retrieving information and for writing and generating text in a single, user-friendly interface.

Related papers

• Text-like Encoding of Collaborative Information in Large Language Models for Recommendation [58.87865271693269]
  We introduce BinLLM, a novel method to seamlessly integrate collaborative information with Large Language Models for Recommendation (LLMRec) BinLLM converts collaborative embeddings from external models into binary sequences. BinLLM provides options to compress the binary sequence using dot-decimal notation to avoid excessively long lengths.
  arXiv  Detail & Related papers  (2024-06-05T12:45:25Z)
• S3LLM: Large-Scale Scientific Software Understanding with LLMs using Source, Metadata, and Document [8.518000504951404]
  Large language models (LLMs) provide novel pathways for understanding complex scientific codes. S3LLM is a framework designed to enable the examination of source code, code metadata, and summarized information in an interactive, conversational manner. S3LLM demonstrates the potential of using locally deployed open-source LLMs for the rapid understanding of large-scale scientific computing software.
  arXiv  Detail & Related papers  (2024-03-15T17:04:27Z)
• Large Language User Interfaces: Voice Interactive User Interfaces powered by LLMs [5.06113628525842]
  We present a framework that can serve as an intermediary between a user and their user interface (UI) We employ a system that stands upon textual semantic mappings of UI components, in the form of annotations. Our engine can classify the most appropriate application, extract relevant parameters, and subsequently execute precise predictions of the user's expected actions.
  arXiv  Detail & Related papers  (2024-02-07T21:08:49Z)
• Unitxt: Flexible, Shareable and Reusable Data Preparation and Evaluation for Generative AI [15.220987187105607]
  Unitxt is an innovative library for customizable textual data preparation and evaluation tailored to generative language models. Unitxt integrates with common libraries like HFace and LM-eval-harness, enabling easy customization and sharing between practitioners. Beyond being a tool, Unitxt is a community-driven platform, empowering users to build, share, and advance their pipelines.
  arXiv  Detail & Related papers  (2024-01-25T08:57:33Z)
• An Interactive Query Generation Assistant using LLM-based Prompt Modification and User Feedback [9.461978375200102]
  The proposed interface is a novel search interface which supports automatic and interactive query generation over a mono-linguial or multi-lingual document collection. The interface enables the users to refine the queries generated by different LLMs, to provide feedback on the retrieved documents or passages, and is able to incorporate the users' feedback as prompts to generate more effective queries.
  arXiv  Detail & Related papers  (2023-11-19T04:42:24Z)
• mPLUG-DocOwl: Modularized Multimodal Large Language Model for Document Understanding [55.4806974284156]
  Document understanding refers to automatically extract, analyze and comprehend information from digital documents, such as a web page. Existing Multi-model Large Language Models (MLLMs) have demonstrated promising zero-shot capabilities in shallow OCR-free text recognition.
  arXiv  Detail & Related papers  (2023-07-04T11:28:07Z)
• Macaw-LLM: Multi-Modal Language Modeling with Image, Audio, Video, and Text Integration [50.94902442781148]
  We propose a novel multi-modal large language model (LLM) that seamlessly integrates visual, audio, and textual information. Macaw-LLM consists of three main components: a modality module for encoding multi-modal data, a cognitive module for harnessing pretrained LLMs, and an alignment module for harmonizing diverse representations. We construct a large-scale multi-modal instruction dataset in terms of multi-turn dialogue, including 69K image instances and 50K video instances.
  arXiv  Detail & Related papers  (2023-06-15T12:45:25Z)
• CodeTF: One-stop Transformer Library for State-of-the-art Code LLM [72.1638273937025]
  We present CodeTF, an open-source Transformer-based library for state-of-the-art Code LLMs and code intelligence. Our library supports a collection of pretrained Code LLM models and popular code benchmarks. We hope CodeTF is able to bridge the gap between machine learning/generative AI and software engineering.
  arXiv  Detail & Related papers  (2023-05-31T05:24:48Z)
• Frugal Prompting for Dialog Models [17.048111072193933]
  This study examines different approaches for building dialog systems using large language models (LLMs) As part of prompt tuning, we experiment with various ways of providing instructions, exemplars, current query and additional context. The research also analyzes the representations of dialog history that have the optimal usable-information density.
  arXiv  Detail & Related papers  (2023-05-24T09:06:49Z)
• Learning Label Modular Prompts for Text Classification in the Wild [56.66187728534808]
  We propose text classification in-the-wild, which introduces different non-stationary training/testing stages. Decomposing a complex task into modular components can enable robust generalisation under such non-stationary environment. We propose MODULARPROMPT, a label-modular prompt tuning framework for text classification tasks.
  arXiv  Detail & Related papers  (2022-11-30T16:26:38Z)
• Text Modular Networks: Learning to Decompose Tasks in the Language of Existing Models [61.480085460269514]
  We propose a framework for building interpretable systems that learn to solve complex tasks by decomposing them into simpler ones solvable by existing models. We use this framework to build ModularQA, a system that can answer multi-hop reasoning questions by decomposing them into sub-questions answerable by a neural factoid single-span QA model and a symbolic calculator.
  arXiv  Detail & Related papers  (2020-09-01T23:45:42Z)

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.