Papers by Elnaz Nouri
Solving Data-centric Tasks using Large Language Models (2024.findings-naacl)
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Shraddha Barke, Christian Poelitz, Carina Negreanu, Benjamin Zorn, José Cambronero, Andrew Gordon, Vu Le, Elnaz Nouri, Nadia Polikarpova, Advait Sarkar, Brian Slininger, Neil Toronto, Jack Williams
| Challenge: | Large language models are increasingly useful for data-centric tasks, but how do we decide how much data to include in the prompt? |
| Approach: | They propose a cluster-then-select prompting technique that adds the most representative rows from the input data to the LLM prompt. |
| Outcome: | The proposed technique outperforms a baseline for tasks with syntactic variation in the input table. |
A Recipe for Creating Multimodal Aligned Datasets for Sequential Tasks (2020.acl-main)
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| Challenge: | a web-based algorithm can be used to align instructions for different tasks . video instructions can be noisy and contain far more information than textual instructions. |
| Approach: | They propose an algorithm that learns pairwise alignments between different recipes . they then use a graph algorithm to derive a joint alignment between multiple video and text recipes based on the same recipe. |
| Outcome: | The proposed algorithm learns pairwise alignments between different recipes for the same dish. |
InstructExcel: A Benchmark for Natural Language Instruction in Excel (2023.findings-emnlp)
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Justin Payan, Swaroop Mishra, Mukul Singh, Carina Negreanu, Christian Poelitz, Chitta Baral, Subhro Roy, Rasika Chakravarthy, Benjamin Van Durme, Elnaz Nouri
| Challenge: | Large Language Models (LLMs) can solve increasingly complex NLP tasks such as Excel specific tasks. |
| Approach: | They propose a large-scale benchmark to test whether Large Language Models can generate code that solves Excel specific tasks provided via natural language user instructions. |
| Outcome: | The proposed model outperforms existing models and provides a hard benchmark for state of the art models like GPT-4. |
Reinforcement Guided Multi-Task Learning Framework for Low-Resource Stereotype Detection (2022.acl-long)
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| Challenge: | Existing ‘Stereotype Detection’ datasets adopt a diagnostic approach toward large PLMs. |
| Approach: | They propose a multi-task model that leverages the abundance of data-rich neighboring tasks such as hate speech detection, offensive language detection, misogyny detection, etc., to improve the empirical performance. |
| Outcome: | The proposed model achieves significant gains over baselines on hate speech detection, offensive language detection, misogyny detection, etc. |
HELP ME THINK: A Simple Prompting Strategy for Non-experts to Create Customized Content with Models (2023.findings-acl)
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| Challenge: | Existing prompting techniques for providing control are task-specific and lack generality; this limits their adoption among non-expert users. |
| Approach: | They propose a prompting strategy that encourages large language models to help non-expert users by asking relevant questions and leveraging user answers to execute a task. |
| Outcome: | The proposed prompting strategy is able to help non-expert users with a variety of tasks. |