no code implementations • 27 Feb 2024 • Keshav Ramji, Young-suk Lee, Ramón Fernandez Astudillo, Md Arafat Sultan, Tahira Naseem, Asim Munawar, Radu Florian, Salim Roukos
It is often desirable for Large Language Models (LLMs) to capture multiple objectives when providing a response.
no code implementations • 19 Feb 2024 • Md Arafat Sultan, Jatin Ganhotra, Ramón Fernandez Astudillo
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).
no code implementations • 4 Feb 2024 • Gaurav Pandey, Yatin Nandwani, Tahira Naseem, Mayank Mishra, Guangxuan Xu, Dinesh Raghu, Sachindra Joshi, Asim Munawar, Ramón Fernandez Astudillo
Following the success of Proximal Policy Optimization (PPO) for Reinforcement Learning from Human Feedback (RLHF), new techniques such as Sequence Likelihood Calibration (SLiC) and Direct Policy Optimization (DPO) have been proposed that are offline in nature and use rewards in an indirect manner.
1 code implementation • 21 Oct 2023 • Young-suk Lee, Md Arafat Sultan, Yousef El-Kurdi, Tahira Naseem Asim Munawar, Radu Florian, Salim Roukos, Ramón Fernandez Astudillo
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.
no code implementations • 24 Apr 2023 • Young-suk Lee, Ramón Fernandez Astudillo, Radu Florian, Tahira Naseem, Salim Roukos
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.
1 code implementation • NAACL 2022 • Tahira Naseem, Austin Blodgett, Sadhana Kumaravel, Tim O'Gorman, Young-suk Lee, Jeffrey Flanigan, Ramón Fernandez Astudillo, Radu Florian, Salim Roukos, Nathan Schneider
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 representation and evaluation.
1 code implementation • EMNLP 2021 • Jiawei Zhou, Tahira Naseem, Ramón Fernandez Astudillo, Young-suk Lee, Radu Florian, Salim Roukos
We provide a detailed comparison with recent progress in AMR parsing and show that the proposed parser retains the desirable properties of previous transition-based approaches, while being simpler and reaching the new parsing state of the art for AMR 2. 0, without the need for graph re-categorization.
Ranked #9 on AMR Parsing on LDC2017T10 (using extra training data)
1 code implementation • ACL 2021 • Peng Qian, Tahira Naseem, Roger Levy, Ramón Fernandez Astudillo
Here we study whether structural guidance leads to more human-like systematic linguistic generalization in Transformer language models without resorting to pre-training on very large amounts of data.
1 code implementation • NAACL 2021 • Jiawei Zhou, Tahira Naseem, Ramón Fernandez Astudillo, Radu Florian
In this work, we propose a transition-based system that combines hard-attention over sentences with a target-side action pointer mechanism to decouple source tokens from node representations and address alignments.
Ranked #1 on AMR Parsing on LDC2014T12
8 code implementations • 5 Feb 2016 • André F. T. Martins, Ramón Fernandez Astudillo
We propose sparsemax, a new activation function similar to the traditional softmax, but able to output sparse probabilities.
1 code implementation • EMNLP 2015 • Wang Ling, Tiago Luís, Luís Marujo, Ramón Fernandez Astudillo, Silvio Amir, Chris Dyer, Alan W. black, Isabel Trancoso
We introduce a model for constructing vector representations of words by composing characters using bidirectional LSTMs.
Ranked #4 on Part-Of-Speech Tagging on Penn Treebank