L-TUNING: Synchronized Label Tuning for Prompt and Prefix in LLMs
Stevens Institute of Technology（史蒂文斯理工学院）；Hajee Mohammad Danesh Science & Technology University（哈吉·穆罕默德·达内什科学技术大学）；Noakhali Science & Technology University（诺阿卡利科学技术大学）；Amazon（亚马逊）
Efficiently fine-tuning Large Language Models (LLMs) for specific tasks presents a considerable challenge in natural language processing. Traditional methods, like prompt or prefix tuning, typically rely on arbitrary tokens for training, leading to prolonged training times and generalized token use across various class labels. To address these issues, this paper introduces L-Tuning, an efficient fine-tuning approach designed for classification tasks within the Natural Language Inference (NLI) framework. Diverging from conventional methods, L-Tuning focuses on the fine-tuning of label tokens processed through a pre-trained LLM, thereby harnessing its pre-existing semantic knowledge. This technique not only improves the fine-tuning accuracy and efficiency but also facilitates the generation of distinct label embeddings for each class, enhancing the model's training nuance. Our experimental results indicate a significant improvement in training efficiency and classification accuracy with L-Tuning compared to traditional approaches, marking a promising advancement in fine-tuning LLMs for complex language tasks.
Fine-tuning is the de facto way to leverage large pretrained language models to perform downstream tasks. However, it modifies all the language model parameters and therefore necessitates storing a full copy for each task. In this paper, we propose prefix-tuning, a lightweight alternative to fine-tuning for natural language generation tasks, which keeps language model parameters frozen, but optimizes a small continuous task-specific vector (called the prefix). Prefix-tuning draws inspiration from prompting, allowing subsequent tokens to attend to this prefix as if it were "virtual tokens". We …
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Amazon Alexa AI（亚马逊Alexa人工智能部门）
Prefix-tuning, or more generally continuous prompt tuning, has become an essential paradigm of parameter-efficient transfer learning. Using a large pre-trained language model (PLM), prefix-tuning can obtain strong performance by training only a small portion of parameters. In this paper, we propose to understand and further develop prefix-tuning through the kernel lens. Specifically, we make an analogy between \textit{prefixes} and \textit{inducing variables} in kernel methods and hypothesize that \textit{prefixes} serving as \textit{inducing variables} would improve their overall mechanism. F…
Unlocking Memorization in Large Language Models with Dynamic Soft Prompting
George Mason University（乔治梅森大学）；GE Healthcare（GE医疗）；University of Pittsburgh（匹兹堡大学）；William and Mary（威廉玛丽学院）；Southern University of Science and Technology（南方科技大学）；Florida State University（佛罗里达州立大学）
Determining the role of event arguments is a crucial subtask of event extraction. Most previous supervised models leverage costly annotations, which is not practical for open-domain applications. In this work, we propose to use global constraints with prompting to effectively tackles event argument classification without any annotation and task-specific training. Specifically, given an event and its associated passage, the model first creates several new passages by prefix prompts and cloze prompts, where prefix prompts indicate event type and trigger span, and cloze prompts connect each candi…
International Institute of Information Technology, Bangalore（班加罗尔国际信息技术学院）；Indraprastha Institute of Information Technology, Delhi（德里印德拉普拉斯塔信息技术学院）；International Institute of Information Technology, Hyderabad（海得拉巴国际信息技术学院）
Online news and social media have been the de facto mediums to disseminate information globally from the beginning of the last decade. However, bias in content and purpose of intentions are not regulated, and managing bias is the responsibility of content consumers. In this regard, understanding the stances and biases of news sources towards specific entities becomes important. To address this problem, we use pretrained language models, which have been shown to bring about good results with no task-specific training or few-shot training. In this work, we approach the problem of characterizing …
Nagoya University（名古屋大学）；Nara Institute of Science and Technology（奈良科学技術大學）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；RIKEN AIP（理化学研究所(AIP)）
Ethicist: Targeted Training Data Extraction Through Loss Smoothed Soft Prompting and Calibrated Confidence Estimation
Institute for Artificial Intelligence（人工智能研究院）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；Tsinghua University（清华大学）
Beyond Retrieval: Learning Compact User Representations for Scalable LLM Personalization
Microsoft（微软公司）；Shanghai International Studies University（上海国际问题研究大学）；Zhejiang University（浙江大学）；Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University（数据科学与人工智能系，香港理工大学）
Self-Control of LLM Behaviors by Compressing Suffix Gradient into Prefix Controller
Shenzhen University（深圳大学）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Tsinghua University（清华大学）；The University of Hong Kong（香港大学）；California Institute of Technology（加州理工学院）
On Unified Prompt Tuning for Request Quality Assurance in Public Code Review
School of Computer Science and Artificial Intelligence, Wuhan University of Technology（武汉理工大学计算机科学与人工智能学院）；School of Computer Science, Wuhan University（武汉大学计算机学院）
CacheRouter: A Dual-Path Tool Routing Architecture with Cache-Preserving Main-Model Isolation for Long-Tail Tool Discovery
School of Mathematical Sciences, Beijing University of Posts and Telecommunications（北京邮电大学数学科学学院）；School of Mathematics and Statistics, Chongqing University（重庆大学数学与统计学院）；School of Science, Beijing Forestry University（北京林业大学理学院）
