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Christian Poelitz

4 accepted papers

2025

Evaluating the Evaluator: Measuring LLMs’ Adherence to Task Evaluation Instructions

AAAI 2025technical

LLMs-as-a-judge is a recently popularized method which replaces human judgements in task evaluation with automatic evaluation using LLMs. Due to widespread use of RLHF (Reinforcement Learning from Human Feedback), state-of-the-art LLMs like GPT4 and Llama3 are expected to have strong alignment with…

Cited by 10SourcePDFScholar
2025

MAGIC: Generating Self-Correction Guideline for In-Context Text-to-SQL

AAAI 2025technical

Self-correction in text-to-SQL is the process of prompting large language model (LLM) to revise its previously incorrectly generated SQL, and commonly relies on manually crafted self-correction guidelines by human experts that are not only labor-intensive to produce but also limited by the human abi…

2024

Solving Data-centric Tasks using Large Language Models

NAACL 2024findings

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 onl…

2023

InstructExcel: A Benchmark for Natural Language Instruction in Excel

EMNLP 2023long findings

With the evolution of Large Language Models (LLMs) we can solve increasingly more complex NLP tasks across various domains, including spreadsheets. This work investigates whether LLMs can generate code (Excel OfficeScripts, a TypeScript API for executing many tasks in Excel) that solves Excel specif…

Cited by 0SourceScholar