Selecting Context Clozes for Lightweight Reading Compliance
We explore a novel approach to reading compliance, leveraging large language models to select inline challenges that discourage skipping during reading. This lightweight ‘testing’ is accomplished through automatically identified context clozes where the reader must supply a missing word that would be hard to guess if earlier material was skipped. Clozes are selected by scoring each word by the contrast between its likelihood with and without prior sentences as context, preferring to leave gaps where this contrast is high. We report results of an initial human-participant test that indicates this method can find clozes that have this property.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Few-shot Policy (de)composition in Conversational Question Answering
The task of policy compliance detection (PCD) is to determine if a scenario is in compliance with respect to a set of written policies. In a conversational setting, the results of PCD can indicate if clarifying questions…
Conversational Question AnsweringQuestion AnsweringReading ComprehensionBroad Context Language Modeling as Reading Comprehension
Progress in text understanding has been driven by large datasets that test particular capabilities, like recent datasets for reading comprehension (Hermann et al., 2015). We focus here on the LAMBADA dataset (Paperno et …
coreference-resolutionCoreference ResolutionLAMBADALanguage Modeling+3ContextSpeech: Expressive and Efficient Text-to-Speech for Paragraph Reading
While state-of-the-art Text-to-Speech systems can generate natural speech of very high quality at sentence level, they still meet great challenges in speech generation for paragraph / long-form reading. Such deficiencies…
FormSentencetext-to-speechText to SpeechA Lightweight Transmission Parameter Selection Scheme Using Reinforcement Learning for LoRaWAN
The number of IoT devices is predicted to reach 125 billion by 2023. The growth of IoT devices will intensify the collisions between devices, degrading communication performance. Selecting appropriate transmission parame…
Fairnessreinforcement-learningReinforcement Learning (RL)Prompt-and-Check: Using Large Language Models to Evaluate Communication Protocol Compliance in Simulation-Based Training
Accurate evaluation of procedural communication compliance is essential in simulation-based training, particularly in safety-critical domains where adherence to compliance checklists reflects operational competence. This…