Zobrazeno 1 - 10
of 36
pro vyhledávání: '"Astudillo, Ramón Fernandez"'
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:
Don-Yehiya, Shachar, Burtenshaw, Ben, Astudillo, Ramon Fernandez, Osborne, Cailean, Jaiswal, Mimansa, Kuo, Tzu-Sheng, Zhao, Wenting, Shenfeld, Idan, Peng, Andi, Yurochkin, Mikhail, Kasirzadeh, Atoosa, Huang, Yangsibo, Hashimoto, Tatsunori, Jernite, Yacine, Vila-Suero, Daniel, Abend, Omri, Ding, Jennifer, Hooker, Sara, Kirk, Hannah Rose, Choshen, Leshem
Human feedback on conversations with language language models (LLMs) is central to how these systems learn about the world, improve their capabilities, and are steered toward desirable and safe behaviors. However, this feedback is mostly collected by
Externí odkaz:
http://arxiv.org/abs/2408.16961
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
We introduce a structured chain-of-thought (SCoT) prompting approach to generating content-grounded multi-turn question-answer conversations using a pre-trained large language model (LLM). At the core of our proposal is a structured breakdown of the
Externí odkaz:
http://arxiv.org/abs/2402.11770
BRAIn: Bayesian Reward-conditioned Amortized Inference for natural language generation from feedback
Autor:
Pandey, Gaurav, Nandwani, Yatin, Naseem, Tahira, Mishra, Mayank, Xu, Guangxuan, Raghu, Dinesh, Joshi, Sachindra, Munawar, Asim, Astudillo, Ramón Fernandez
Distribution matching methods for language model alignment such as Generation with Distributional Control (GDC) and Distributional Policy Gradient (DPG) have not received the same level of attention in reinforcement learning from human feedback (RLHF
Externí odkaz:
http://arxiv.org/abs/2402.02479
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:
Drozdov, Andrew, Zhou, Jiawei, Florian, Radu, McCallum, Andrew, Naseem, Tahira, Kim, Yoon, Astudillo, Ramon Fernandez
Transition-based parsers for Abstract Meaning Representation (AMR) rely on node-to-word alignments. These alignments are learned separately from parser training and require a complex pipeline of rule-based components, pre-processing, and post-process
Externí odkaz:
http://arxiv.org/abs/2205.01464
Autor:
Naseem, Tahira, Blodgett, Austin, Kumaravel, Sadhana, O'Gorman, Tim, Lee, Young-Suk, Flanigan, Jeffrey, Astudillo, Ramón Fernandez, Florian, Radu, Roukos, Salim, Schneider, Nathan
Despite extensive research on parsing of English sentences into Abstraction Meaning Representation (AMR) graphs, which are compared to gold graphs via the Smatch metric, full-document parsing into a unified graph representation lacks well-defined rep
Externí odkaz:
http://arxiv.org/abs/2112.08513