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Found 8 papers, 2 papers with code
LENS: A Learnable Evaluation Metric for Text Simplification
Training learnable metrics using modern language models has recently emerged as a promising method for the automatic evaluation of machine translation.
MultiTalk: A Highly-Branching Dialog Testbed for Diverse Conversations
We study conversational dialog in which there are many possible responses to a given history.
Is GPT-3 Text Indistinguishable from Human Text? Scarecrow: A Framework for Scrutinizing Machine Text
To support the broad range of real machine errors that can be identified by laypeople, the ten error categories of Scarecrow -- such as redundancy, commonsense errors, and incoherence -- are identified through several rounds of crowd annotation experiments without a predefined ontology.
Improving Large-scale Paraphrase Acquisition and Generation
This paper addresses the quality issues in existing Twitter-based paraphrase datasets, and discusses the necessity of using two separate definitions of paraphrase for identification and generation tasks.
Dancing Between Success and Failure: Edit-level Simplification Evaluation using SALSA
Large language models (e. g., GPT-4) are uniquely capable of producing highly rated text simplification, yet current human evaluation methods fail to provide a clear understanding of systems' specific strengths and weaknesses.
Thresh: A Unified, Customizable and Deployable Platform for Fine-Grained Text Evaluation
Additionally, we introduce a Python library to streamline the entire process from typology design and deployment to annotation processing.
Automatic and Human-AI Interactive Text Generation
In this tutorial, we focus on text-to-text generation, a class of natural language generation (NLG) tasks, that takes a piece of text as input and then generates a revision that is improved according to some specific criteria (e. g., readability or linguistic styles), while largely retaining the original meaning and the length of the text.
Reducing Privacy Risks in Online Self-Disclosures with Language Models
Motivated by the user feedback, we introduce the task of self-disclosure abstraction, which is paraphrasing disclosures into less specific terms while preserving their utility, e. g., "Im 16F" to "I'm a teenage girl".
