반응형 페이퍼572 [2023-08-03] 오늘의 자연어처리 Advancing Beyond Identification: Multi-bit Watermark for Language Models This study aims to proactively tackle misuse of large language models beyond identification of machine-generated text. While existing methods focus on detection, some malicious misuses demand tracing the adversary user for counteracting them. To address this, we propose "Multi-bit Watermark through Color-listing" (COLOR), e.. 2023. 8. 3. [2023-08-02] 오늘의 자연어처리 LLMediator: GPT-4 Assisted Online Dispute Resolution In this article, we introduce LLMediator, an experimental platform designed to enhance online dispute resolution (ODR) by utilizing capabilities of state-of-the-art large language models (LLMs) such as GPT-4. In the context of high-volume, low-intensity legal disputes, alternative dispute resolution methods such as negotiation and mediation of.. 2023. 8. 2. [2023-08-01] 오늘의 자연어처리 Investigating the Learning Behaviour of In-context Learning: A Comparison with Supervised Learning Large language models (LLMs) have shown remarkable capacity for in-context learning (ICL), where learning a new task from just a few training examples is done without being explicitly pre-trained. However, despite the success of LLMs, there has been little understanding of how ICL learns the knowle.. 2023. 8. 1. [2023-07-31] 오늘의 자연어처리 What Makes a Good Paraphrase: Do Automated Evaluations Work? Paraphrasing is the task of expressing an essential idea or meaning in different words. But how different should the words be in order to be considered an acceptable paraphrase? And can we exclusively use automated metrics to evaluate the quality of a paraphrase? We attempt to answer these questions by conducting experiments on a Germa.. 2023. 7. 31. 이전 1 ··· 38 39 40 41 42 43 44 ··· 143 다음 반응형