Papers by Ahmed Awadallah
WALNUT: A Benchmark on Semi-weakly Supervised Learning for Natural Language Understanding (2022.naacl-main)
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| Challenge: | Existing studies on weak supervision for NLU focus on a specific task or simulate weak supervision signals from ground-truth labels. |
| Approach: | They propose a benchmark to advocate and facilitate research on weak supervision for NLU . they use document-level and token-level prediction tasks as examples . |
| Outcome: | The proposed benchmark advocates and facilitates research on weak supervision for NLU tasks. |
DYLE: Dynamic Latent Extraction for Abstractive Long-Input Summarization (2022.acl-long)
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Ziming Mao, Chen Henry Wu, Ansong Ni, Yusen Zhang, Rui Zhang, Tao Yu, Budhaditya Deb, Chenguang Zhu, Ahmed Awadallah, Dragomir Radev
| Challenge: | Existing models struggle with summarizing long text due to high memory complexity of the full self-attention. |
| Approach: | They propose a dynamic latent extraction approach for abstractive long-input summarization that treats extracted text snippets as latent variables and allows dynamic attention weights during decoding. |
| Outcome: | The proposed method outperforms existing methods on GovReport, QMSum, and arXiv while yielding strong results on arX. |
NL-EDIT: Correcting Semantic Parse Errors through Natural Language Interaction (2021.naacl-main)
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Ahmed Elgohary, Christopher Meek, Matthew Richardson, Adam Fourney, Gonzalo Ramos, Ahmed Hassan Awadallah
| Challenge: | Existing systems frame semantic parsing as a one-shot translation from a natural language question to the logical form. |
| Approach: | They propose a model that uses natural language feedback to correct parsers . they show that NL-EDIT can boost the accuracy of existing parser by 20% . |
| Outcome: | The proposed model can boost parsers' accuracy by 20% with just one turn of correction. |
Assessing and Verifying Task Utility in LLM-Powered Applications (2024.emnlp-main)
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Negar Arabzadeh, Siqing Huo, Nikhil Mehta, Qingyun Wu, Chi Wang, Ahmed Awadallah, Charles Clarke, Julia Kiseleva
| Challenge: | Rapid development of Large Language Models (LLMs) has led to a surge in applications that facilitate collaboration among multiple agents, assisting humans in their daily tasks. |
| Approach: | They propose a framework to propose criteria tailored to the unique purpose of any given application and propose corresponding criteria for the application. |
| Outcome: | The proposed framework provides a comprehensive assessment of the effectiveness and robustness of two open source datasets including Math Problem solving and ALFWorld House-hold related tasks. |
Axiomatic Preference Modeling for Longform Question Answering (2023.emnlp-main)
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| Challenge: | Recent advances in large language models have helped bridge the "alignment gap" between the responses of raw pretrained language models and responses that resonate more closely with human preferences. |
| Approach: | They propose to use a axiomatic framework to generate a rich variety of preference signals to uphold these signals. |
| Outcome: | The proposed model outperforms GPT-4 and ChatGPT in preference scoring. |
Automatic Pair Construction for Contrastive Post-training (2024.findings-naacl)
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Canwen Xu, Corby Rosset, Ethan Chau, Luciano Corro, Shweti Mahajan, Julian McAuley, Jennifer Neville, Ahmed Awadallah, Nikhil Rao
| Challenge: | Large language models (LLMs) have unprecedented proficiency in a wide array of tasks. |
| Approach: | They propose a way to construct contrastive data using preference pairs from multiple models of varying strengths using SLiC and DPO. |
| Outcome: | The proposed method outperforms existing models like Orca in the comparison of SLiC and DPO with SFT baselines. |
SummN: A Multi-Stage Summarization Framework for Long Input Dialogues and Documents (2022.acl-long)
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Yusen Zhang, Ansong Ni, Ziming Mao, Chen Henry Wu, Chenguang Zhu, Budhaditya Deb, Ahmed Awadallah, Dragomir Radev, Rui Zhang
| Challenge: | Existing methods to handle long text are limited due to time and memory complexity and limited input lengths. |
| Approach: | They propose a multi-stage split-then-summarize framework for long input summarization . their framework can process input text of arbitrary length by adjusting the number of stages . |
| Outcome: | The proposed framework outperforms existing methods on three long meeting summarization datasets and on a long document summarizing dataset. |
LiST: Lite Prompted Self-training Makes Parameter-efficient Few-shot Learners (2022.findings-naacl)
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| Challenge: | LiST is an efficient method for fine-tuning large pre-trained language models in few-shot learning settings. |
| Approach: | They propose a method for efficient fine-tuning of large pre-trained language models in few-shot settings using self-training and meta-learning. |
| Outcome: | The proposed method outperforms GPT-3 in-context learning by 33% on few-shot tasks. |
Improving Grounded Language Understanding in a Collaborative Environment by Interacting with Agents Through Help Feedback (2024.findings-eacl)
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| Challenge: | In many approaches to Natural Language Processing tasks, language is inherently interactive. |
| Approach: | They propose to use human-AI collaboration to improve human-human interaction by providing feedback that the agent can understand and utilize. |
| Outcome: | The proposed task is an interactive grounded language understanding task in a MineCraft-like world. |