Papers by Nghia Ngo
ULLME: A Unified Framework for Large Language Model Embeddings with Generation-Augmented Learning (2024.emnlp-demo)
Copied to clipboard
| Challenge: | Existing frameworks for large language model embeddings have limited support for only a limited range of architectures and fine-tuning strategies. |
| Approach: | They propose a framework that enables bidirectional attention across various LLMs and supports a range of fine-tuning strategies. |
| Outcome: | The proposed framework enables bidirectional attention across various LLMs and supports a range of fine-tuning strategies. |
Unsupervised Domain Adaptation for Joint Information Extraction (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Current JIE methods focus on standard supervised learning setting where training and test data come from the same domain. |
| Approach: | They propose a method to induce domain-invariant representations for the tasks in JIE by a generalized version of domain-adversarial learning. |
| Outcome: | The proposed method improves out-of-domain performance for current pipeline approaches for all IE tasks. |
FAMIE: A Fast Active Learning Framework for Multilingual Information Extraction (2022.naacl-demo)
Copied to clipboard
| Challenge: | Existing active learning frameworks require long time between annotation batches due to time-consuming nature of model training and data selection. |
| Approach: | They propose a small proxy network to synchronize the proxy network with the main large model to ensure appropriateness of the selected annotation examples for the main model. |
| Outcome: | The proposed framework can support multiple languages and is available on github and demo website. |
Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback (2023.emnlp-demo)
Copied to clipboard
| Challenge: | Existing instruction-tuned open-source LLMs have only been instruction- tuned for English and a few popular languages, thus hindering their accessibility to many other languages in the world. |
| Approach: | They propose a framework that uses supervised fine-tuning and reinforcement learning from human feedback to improve the accessibility of large language models. |
| Outcome: | The proposed framework enables the evaluation of generative LLMs in multiple languages. |
ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learning (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Recent advances in natural language processing (NLP) have led to significant breakthroughs in the field. |
| Approach: | They evaluate ChatGPT over multiple tasks with diverse languages and large datasets to provide more comprehensive information for multilingual NLP applications. |
| Outcome: | The proposed model can process and generate texts for multiple languages due to its multilingual training data. |