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Found 10 papers, 3 papers with code
Improving Minimum Bayes Risk Decoding with Multi-Prompt
While instruction fine-tuned LLMs are effective text generators, sensitivity to prompt construction makes performance unstable and sub-optimal in practice.
GPT-4 Jailbreaks Itself with Near-Perfect Success Using Self-Explanation
Research on jailbreaking has been valuable for testing and understanding the safety and security issues of large language models (LLMs).
Reducing Privacy Risks in Online Self-Disclosures with Language Models
Self-disclosure, while being common and rewarding in social media interaction, also poses privacy risks.
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.
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.
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.
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.
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.
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.
MultiTalk: A Highly-Branching Dialog Testbed for Diverse Conversations
We study conversational dialog in which there are many possible responses to a given history.
