Advait Sarkar


2024

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Solving Data-centric Tasks using Large Language Models
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
Findings of the Association for Computational Linguistics: NAACL 2024

Large language models are rapidly replacing help forums like StackOverflow, and are especially helpful to non-professional programmers and end users. These users are often interested in data-centric tasks, like spreadsheet manipulation and data wrangling, which are hard to solve if the intent is only communicated using a natural-language description, without including data. But how do we decide how much data and which data to include in the prompt?This paper makes two contributions towards answering this question. First, we create a dataset of real-world NL-to-code tasks manipulating tabular data, mined from StackOverflow posts. Second, we introduce a novel cluster-then-select prompting technique, which adds the most representative rows from the input data to the LLM prompt. Our experiments show that LLM performance is indeed sensitive to the amount of data passed in the prompt, and that for tasks with a lot of syntactic variation in the input table,our cluster-then-select technique outperforms a random selection baseline.

2019

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Comparative judgments are more consistent than binary classification for labelling word complexity
Sian Gooding | Ekaterina Kochmar | Advait Sarkar | Alan Blackwell
Proceedings of the 13th Linguistic Annotation Workshop

Lexical simplification systems replace complex words with simple ones based on a model of which words are complex in context. We explore how users can help train complex word identification models through labelling more efficiently and reliably. We show that using an interface where annotators make comparative rather than binary judgments leads to more reliable and consistent labels, and explore whether comparative judgments may provide a faster way for collecting labels.