Beyond the Chat: Executable and Verifiable Text-Editing with LLMs

• URL: http://arxiv.org/abs/2309.15337v1
• Date: Wed, 27 Sep 2023 00:56:17 GMT
• Title: Beyond the Chat: Executable and Verifiable Text-Editing with LLMs
• Authors: Philippe Laban, Jesse Vig, Marti A. Hearst, Caiming Xiong, Chien-Sheng Wu
• Abstract summary: Conversational interfaces powered by Large Language Models (LLMs) have recently become a popular way to obtain feedback during document editing. We present InkSync, an editing interface that suggests executable edits directly within the document being edited.
• Score: 87.84199761550634
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conversational interfaces powered by Large Language Models (LLMs) have recently become a popular way to obtain feedback during document editing. However, standard chat-based conversational interfaces do not support transparency and verifiability of the editing changes that they suggest. To give the author more agency when editing with an LLM, we present InkSync, an editing interface that suggests executable edits directly within the document being edited. Because LLMs are known to introduce factual errors, Inksync also supports a 3-stage approach to mitigate this risk: Warn authors when a suggested edit introduces new information, help authors Verify the new information's accuracy through external search, and allow an auditor to perform an a-posteriori verification by Auditing the document via a trace of all auto-generated content. Two usability studies confirm the effectiveness of InkSync's components when compared to standard LLM-based chat interfaces, leading to more accurate, more efficient editing, and improved user experience.

Related papers

• DocEdit-v2: Document Structure Editing Via Multimodal LLM Grounding [128.92659116774374]
  We introduce DocEdit-v2, a novel framework that performs end-to-end document editing by leveraging Large Multimodal Models (LMMs) It consists of three novel components: (1) Doc2Command, which simultaneously localizes edit regions of interest (RoI) and disambiguates user edit requests into edit commands; (2) LLM-based Command Reformulation prompting to tailor edit commands originally intended for specialized software into edit instructions suitable for generalist LMMs; and (3) Moreover, DocEdit-v2 processes these outputs via Large Multimodal Models like GPT-4V and Gemini, to parse the document layout, execute edits on
  arXiv  Detail & Related papers  (2024-10-21T19:59:04Z)
• Enhance Lifelong Model Editing with Continuous Data-Adapter Association [55.697627106315004]
  Large language models (LLMs) require model editing to efficiently update specific knowledge within them and avoid factual errors. Current approaches manage sequential edits by freezing original parameters and allocating new adapters for each knowledge modification. We propose ELDER, textbfEnhancing textbfLifelong motextbfDel textbfEditing with mixtutextbfRe of Low-Rank Adapter (LoRA)
  arXiv  Detail & Related papers  (2024-08-19T02:27:00Z)
• FactCheck Editor: Multilingual Text Editor with End-to-End fact-checking [1.985242455423935]
  'FactCheck Editor' is an advanced text editor designed to automate fact-checking and correct factual inaccuracies. It supports over 90 languages and utilizes transformer models to assist humans in the labor-intensive process of fact verification.
  arXiv  Detail & Related papers  (2024-04-30T11:55:20Z)
• Editing Conceptual Knowledge for Large Language Models [65.38231526537476]
  This paper pioneers the investigation of editing conceptual knowledge for Large Language Models (LLMs) We construct a novel benchmark dataset ConceptEdit and establish a suite of new metrics for evaluation. experimental results reveal that, although existing editing methods can efficiently modify concept-level definition to some extent, they also have the potential to distort the related instantial knowledge.
  arXiv  Detail & Related papers  (2024-03-10T16:57:10Z)
• GenAudit: Fixing Factual Errors in Language Model Outputs with Evidence [64.95492752484171]
  We present GenAudit -- a tool intended to assist fact-checking LLM responses for document-grounded tasks. We train models to execute these tasks, and design an interactive interface to present suggested edits and evidence to users. To ensure that most errors are flagged by the system, we propose a method that can increase the error recall while minimizing impact on precision.
  arXiv  Detail & Related papers  (2024-02-19T21:45:55Z)
• Knowledge Editing on Black-box Large Language Models [37.17131278142237]
  Knowledge editing aims to efficiently and precisely modify the behavior of large language models (LLMs) to update specific knowledge. Current research primarily focuses on white-box LLMs editing, overlooking an important scenario: black-box LLMs editing. We introduce KE on black-box LLMs and then propose a comprehensive evaluation framework to overcome the limitations of existing evaluations. Experiments and analysis on two benchmarks demonstrate that postEdit outperforms all baselines and achieves strong generalization.
  arXiv  Detail & Related papers  (2024-02-13T17:59:34Z)
• On the Robustness of Editing Large Language Models [57.477943944826904]
  Large language models (LLMs) have played a pivotal role in building communicative AI, yet they encounter the challenge of efficient updates. This work seeks to understand the strengths and limitations of editing methods, facilitating practical applications of communicative AI.
  arXiv  Detail & Related papers  (2024-02-08T17:06:45Z)
• SWEA: Updating Factual Knowledge in Large Language Models via Subject Word Embedding Altering [17.20346072074533]
  Recent model editing is a promising technique for efficiently updating a small amount of knowledge of large language models (LLMs) We propose a detachable and expandable Subject Word Embedding Altering (SWEA) framework, which finds the editing embeddings through token-level matching. We demonstrate the overall state-of-the-art (SOTA) performance of SWEA$oplus$OS on the textscCounterFact and zsRE datasets.
  arXiv  Detail & Related papers  (2024-01-31T13:08:45Z)
• XATU: A Fine-grained Instruction-based Benchmark for Explainable Text Updates [7.660511135287692]
  This paper introduces XATU, the first benchmark specifically designed for fine-grained instruction-based explainable text editing. XATU considers finer-grained text editing tasks of varying difficulty, incorporating lexical, syntactic, semantic, and knowledge-intensive edit aspects. We demonstrate the effectiveness of instruction tuning and the impact of underlying architecture across various editing tasks.
  arXiv  Detail & Related papers  (2023-09-20T04:58:59Z)

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.