113 citations · 119 across the 8 of their papers we have counts for
9 papers · 1 filter
Self-Refinement of Language Models from External Proxy Metrics Feedback
Keshav Ramji, Young-Suk Lee, Ramón Fernandez Astudillo +5
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 respon…
Ensemble-Instruct: Generating Instruction-Tuning Data with a Heterogeneous Mixture of LMs
Young-Suk Lee, Md Arafat Sultan, Yousef El-Kurdi +4
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 conversation…
Slide, Constrain, Parse, Repeat: Synchronous SlidingWindows for Document AMR Parsing
Sadhana Kumaravel, Tahira Naseem, Ramon Fernandez Astudillo +2
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 ap…
AMR Parsing with Instruction Fine-tuned Pre-trained Language Models
Young-Suk Lee, Ramón Fernandez Astudillo, Radu Florian +2
Instruction fine-tuned language models on a collection of instruction annotated datasets (FLAN) have shown highly effective to improve model performance and generalization to unsee…
PrimeQA: The Prime Repository for State-of-the-Art Multilingual Question Answering Research and Development
Avirup Sil, Jaydeep Sen, Bhavani Iyer +12
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 wit…
Do Answers to Boolean Questions Need Explanations? Yes
Sara Rosenthal, Mihaela Bornea, Avirup Sil +2
Existing datasets that contain boolean questions, such as BoolQ and TYDI QA , provide the user with a YES/NO response to the question. However, a one word response is not sufficien…