Papers by Andrew Gordon
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. |
From Test-Taking to Test-Making: Examining LLM Authoring of Commonsense Assessment Items (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) excel in answering questions pertaining to commonsense reasoning and inference. |
| Approach: | They prompt LLMs to generate items in the style of a benchmark for commonsense reasoning . they find that LLM authors that answer COPA items are more successful . |
| Outcome: | The authors' responses to their own items and their own generated items are better than those of the original LLMs. |