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Register a new userHow many data points is a prompt worth?
كم عدد نقاط البيانات هي قيمة سريعة؟
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عند ضبط النماذج المحددة مسبقا للتصنيف، يستخدم الباحثون إما رأس نموذج عام أو موجه خاص بالتنبؤ.جادل مؤيدو المطالبات بأن المطالبات توفر طريقة لحقن إرشادات خاصة بالمهام، وهي مفيدة في أنظمة البيانات المنخفضة.نهدف إلى تقدير هذه الفائدة من خلال اختبار صارم للمطالبات في إعداد عادل: مقارنة بالضبط الدقيق المطالب والرأس في ظروف متساوية في العديد من المهام وأحجام البيانات.من خلال السيطرة على العديد من مصادر ميزة، نجد أن المطالبة تقدم بالفعل فائدة، وأن هذه الفائدة يمكن تحديدها لكل مهمة.تظهر النتائج أن المطالبة غالبا ما تستحق 100 ثانية من نقاط البيانات في المتوسط عبر مهام التصنيف.
When fine-tuning pretrained models for classification, researchers either use a generic model head or a task-specific prompt for prediction. Proponents of prompting have argued that prompts provide a method for injecting task-specific guidance, which is beneficial in low-data regimes. We aim to quantify this benefit through rigorous testing of prompts in a fair setting: comparing prompted and head-based fine-tuning in equal conditions across many tasks and data sizes. By controlling for many sources of advantage, we find that prompting does indeed provide a benefit, and that this benefit can be quantified per task. Results show that prompting is often worth 100s of data points on average across classification tasks.
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In online domain-specific customer service applications, many companies struggle to deploy advanced NLP models successfully, due to the limited availability of and noise in their datasets. While prior research demonstrated the potential of migrating
large open-domain pretrained models for domain-specific tasks, the appropriate (pre)training strategies have not yet been rigorously evaluated in such social media customer service settings, especially under multilingual conditions. We address this gap by collecting a multilingual social media corpus containing customer service conversations (865k tweets), comparing various pipelines of pretraining and finetuning approaches, applying them on 5 different end tasks. We show that pretraining a generic multilingual transformer model on our in-domain dataset, before finetuning on specific end tasks, consistently boosts performance, especially in non-English settings.
Reliable tagging of Temporal Expressions (TEs, e.g., Book a table at L'Osteria for Sunday evening) is a central requirement for Voice Assistants (VAs). However, there is a dearth of resources and systems for the VA domain, since publicly-available te
mporal taggers are trained only on substantially different domains, such as news and clinical text. Since the cost of annotating large datasets is prohibitive, we investigate the trade-off between in-domain data and performance in DA-Time, a hybrid temporal tagger for the English VA domain which combines a neural architecture for robust TE recognition, with a parser-based TE normalizer. We find that transfer learning goes a long way even with as little as 25 in-domain sentences: DA-Time performs at the state of the art on the news domain, and substantially outperforms it on the VA domain.
GPS technology considers the essential tool for establishing geodetic networks.
Static method of GPS is used often in observing geodetic network points. Establishing
geodetic networks using GPS requires accuracy, consistency and economency.
This p
aper discusses influence of observation mode and number of GPS receivers in
accuracy of calculating of coordinates of points. Coordinates of network points are
clculated using tow and three GPS receivers and with diferent methods like radial, traverse,
network.
Comparison between coordinates for network points obtained by several cases is
performed. The differences between coordinates indicate accuracy of network method in
calculating coordinates when three or more receivers are avlible. When tow receivers are
avilable the radial method is the best in accuracy and consistency.
Many real-world problems require the combined application of multiple reasoning abilities---employing suitable abstractions, commonsense knowledge, and creative synthesis of problem-solving strategies. To help advance AI systems towards such capabili
ties, we propose a new reasoning challenge, namely Fermi Problems (FPs), which are questions whose answers can only be approximately estimated because their precise computation is either impractical or impossible. For example, How much would the sea level rise if all ice in the world melted?'' FPs are commonly used in quizzes and interviews to bring out and evaluate the creative reasoning abilities of humans. To do the same for AI systems, we present two datasets: 1) A collection of 1k real-world FPs sourced from quizzes and olympiads; and 2) a bank of 10k synthetic FPs of intermediate complexity to serve as a sandbox for the harder real-world challenge. In addition to question-answer pairs, the datasets contain detailed solutions in the form of an executable program and supporting facts, helping in supervision and evaluation of intermediate steps. We demonstrate that even extensively fine-tuned large-scale language models perform poorly on these datasets, on average making estimates that are off by two orders of magnitude. Our contribution is thus the crystallization of several unsolved AI problems into a single, new challenge that we hope will spur further advances in building systems that can reason.
Bacteriological critical control points (CCPS) for automatic ice cream
industry were identified based on the primary ingradients of such industry,
processing stages and working environment.
Three thousand samples were analyzed during two productio
n seasons.
There were four critical control points in the company in which the study
was conducted, Pasteurization (mix) stage, cold (tanks) stage, freezing stage,
and hardning (tunnel) stage. The end-product did not coincide with the Syrian
standard because of these critical control point, which contributed by 15%,
25%, 35% and 25% respectively, meanwhile the remaining pointes, such as the
used water, choclate, air and workers were not critical control points under the
production conditions of the investigated company.
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