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pro vyhledávání: '"A, Khattab"'
Natural Language Processing (NLP) systems are increasingly taking the form of multi-stage pipelines involving multiple distinct language models (LMs) and prompting strategies. Here we address the question of how to fine-tune such systems to improve t
Externí odkaz:
http://arxiv.org/abs/2407.10930
Vision Transformers (ViTs) have achieved significant advancement in computer vision tasks due to their powerful modeling capacity. However, their performance notably degrades when trained with insufficient data due to lack of inherent inductive biase
Externí odkaz:
http://arxiv.org/abs/2407.07516
Autor:
Xian, Jasper, Samuel, Saron, Khoubsirat, Faraz, Pradeep, Ronak, Sultan, Md Arafat, Florian, Radu, Roukos, Salim, Sil, Avirup, Potts, Christopher, Khattab, Omar
We develop a method for training small-scale (under 100M parameter) neural information retrieval models with as few as 10 gold relevance labels. The method depends on generating synthetic queries for documents using a language model (LM), and the key
Externí odkaz:
http://arxiv.org/abs/2406.11706
Autor:
Opsahl-Ong, Krista, Ryan, Michael J, Purtell, Josh, Broman, David, Potts, Christopher, Zaharia, Matei, Khattab, Omar
Language Model Programs, i.e. sophisticated pipelines of modular language model (LM) calls, are increasingly advancing NLP tasks, but they require crafting prompts that are jointly effective for all modules. We study prompt optimization for LM progra
Externí odkaz:
http://arxiv.org/abs/2406.11695
Many online content portals allow users to ask questions to supplement their understanding (e.g., of lectures). While information retrieval (IR) systems may provide answers for such user queries, they do not directly assist content creators -- such a
Externí odkaz:
http://arxiv.org/abs/2403.03956
We investigate the problem of spectrum sensing in cognitive radios (CRs) when the receivers are equipped with a large array of antennas. We propose and derive three detectors based on the concept of linear spectral statistics (LSS) in the field of ra
Externí odkaz:
http://arxiv.org/abs/2402.14219
We study how to apply large language models to write grounded and organized long-form articles from scratch, with comparable breadth and depth to Wikipedia pages. This underexplored problem poses new challenges at the pre-writing stage, including how
Externí odkaz:
http://arxiv.org/abs/2402.14207
Autor:
D'Oosterlinck, Karel, Khattab, Omar, Remy, François, Demeester, Thomas, Develder, Chris, Potts, Christopher
Multi-label classification problems with thousands of classes are hard to solve with in-context learning alone, as language models (LMs) might lack prior knowledge about the precise classes or how to assign them, and it is generally infeasible to dem
Externí odkaz:
http://arxiv.org/abs/2401.12178
Question answering (QA) is the task of answering questions posed in natural language with free-form natural language answers extracted from a given passage. In the OpenQA variant, only a question text is given, and the system must retrieve relevant p
Externí odkaz:
http://arxiv.org/abs/2401.03590
Autor:
Singhvi, Arnav, Shetty, Manish, Tan, Shangyin, Potts, Christopher, Sen, Koushik, Zaharia, Matei, Khattab, Omar
Chaining language model (LM) calls as composable modules is fueling a new way of programming, but ensuring LMs adhere to important constraints requires heuristic "prompt engineering". We introduce LM Assertions, a programming construct for expressing
Externí odkaz:
http://arxiv.org/abs/2312.13382