Papers by Christian Poelitz
Solving Data-centric Tasks using Large Language Models (2024.findings-naacl)
Copied to clipboard
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. |
InstructExcel: A Benchmark for Natural Language Instruction in Excel (2023.findings-emnlp)
Copied to clipboard
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. |