Zobrazeno 1 - 10
of 125
pro vyhledávání: '"Florian, Radu"'
Autor:
Lee, Young-Suk, Gunasekara, Chulaka, Contractor, Danish, Astudillo, Ramón Fernandez, Florian, Radu
We introduce a technique for multi-document grounded multi-turn synthetic dialog generation that incorporates three main ideas. First, we control the overall dialog flow using taxonomy-driven user queries that are generated with Chain-of-Thought (CoT
Externí odkaz:
http://arxiv.org/abs/2409.11500
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:
Lynn, Teresa, Altakrori, Malik H., Magdy, Samar Mohamed, Das, Rocktim Jyoti, Lyu, Chenyang, Nasr, Mohamed, Samih, Younes, Aji, Alham Fikri, Nakov, Preslav, Godbole, Shantanu, Roukos, Salim, Florian, Radu, Habash, Nizar
The rapid evolution of Natural Language Processing (NLP) has favored major languages such as English, leaving a significant gap for many others due to limited resources. This is especially evident in the context of data annotation, a task whose impor
Externí odkaz:
http://arxiv.org/abs/2404.17342
Retrieval Augmented Generation (RAG) has become a popular application for large language models. It is preferable that successful RAG systems provide accurate answers that are supported by being grounded in a passage without any hallucinations. While
Externí odkaz:
http://arxiv.org/abs/2404.02103
Autor:
Ramji, Keshav, Lee, Young-Suk, Astudillo, Ramón Fernandez, Sultan, Md Arafat, Naseem, Tahira, Munawar, Asim, Florian, Radu, Roukos, Salim
It is often desirable for Large Language Models (LLMs) to capture multiple objectives when providing a response. In document-grounded response generation, for example, agent responses are expected to be relevant to a user's query while also being gro
Externí odkaz:
http://arxiv.org/abs/2403.00827
Autor:
Lee, Young-Suk, Sultan, Md Arafat, El-Kurdi, Yousef, Munawar, Tahira Naseem Asim, Florian, Radu, Roukos, Salim, Astudillo, Ramón Fernandez
Publikováno v:
EMNLP 2023
Using in-context learning (ICL) for data generation, techniques such as Self-Instruct (Wang et al., 2023) or the follow-up Alpaca (Taori et al., 2023) can train strong conversational agents with only a small amount of human supervision. One limitatio
Externí odkaz:
http://arxiv.org/abs/2310.13961
The sliding window approach provides an elegant way to handle contexts of sizes larger than the Transformer's input window, for tasks like language modeling. Here we extend this approach to the sequence-to-sequence task of document parsing. For this,
Externí odkaz:
http://arxiv.org/abs/2305.17273
Instruction fine-tuned language models on a collection of instruction annotated datasets (FLAN) have shown highly effective to improve model performance and generalization to unseen tasks. However, a majority of standard parsing tasks including abstr
Externí odkaz:
http://arxiv.org/abs/2304.12272
Autor:
Saad-Falcon, Jon, Khattab, Omar, Santhanam, Keshav, Florian, Radu, Franz, Martin, Roukos, Salim, Sil, Avirup, Sultan, Md Arafat, Potts, Christopher
Many information retrieval tasks require large labeled datasets for fine-tuning. However, such datasets are often unavailable, and their utility for real-world applications can diminish quickly due to domain shifts. To address this challenge, we deve
Externí odkaz:
http://arxiv.org/abs/2303.00807
Autor:
Sil, Avirup, Sen, Jaydeep, Iyer, Bhavani, Franz, Martin, Fadnis, Kshitij, Bornea, Mihaela, Rosenthal, Sara, McCarley, Scott, Zhang, Rong, Kumar, Vishwajeet, Li, Yulong, Sultan, Md Arafat, Bhat, Riyaz, Florian, Radu, Roukos, Salim
The field of Question Answering (QA) has made remarkable progress in recent years, thanks to the advent of large pre-trained language models, newer realistic benchmark datasets with leaderboards, and novel algorithms for key components such as retrie
Externí odkaz:
http://arxiv.org/abs/2301.09715