# Designing large language model prompts to extract scores from messy text: A shared dataset and challenge

arXiv: https://arxiv.org/abs/2601.18271 · HTML: https://mlsanity.com/paper/2601.18271/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 16 references that are in the map.
- First posted on arXiv 26 Jan 2026.

## Builds on (most cited first, 10 of 10)

- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://mlsanity.com/paper/2206.04615/) (2022)
- [SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems](https://mlsanity.com/paper/1905.00537/) (2019)
- [Large Language Models are Inconsistent and Biased Evaluators](https://mlsanity.com/paper/2405.01724/) (2024)
- [LLM-Rubric: A Multidimensional, Calibrated Approach to Automated Evaluation of Natural Language Texts](https://mlsanity.com/paper/2501.00274/) (2024)
- [In which fields can ChatGPT detect journal article quality? An evaluation of REF2021 results](https://mlsanity.com/paper/2409.16695/) (2024)
- [LASER: An LLM-based ASR Scoring and Evaluation Rubric](https://mlsanity.com/paper/2510.07437/) (2025)
- [Implicit and Explicit Research Quality Score Probabilities from ChatGPT](https://mlsanity.com/paper/2506.13525/) (2025)
- [Can Small and Reasoning Large Language Models Score Journal Articles for Research Quality and Do Averaging and Few-shot Help?](https://mlsanity.com/paper/2510.22389/) (2025)

## Abstract (arXiv)

In some areas of computing, natural language processing and information science, progress is made by sharing datasets and challenging the community to design the best algorithm for an associated task. This article introduces a shared dataset of 1446 short texts, each of which describes a research quality score on the UK scale of 1* to 4*. This is a messy collection, with some texts not containing scores and others including invalid scores or strange formats. With this dataset there is also a description of what constitutes a valid score and a "gold standard" of the correct scores for these texts (including missing values). The challenge is to design a prompt for Large Language Models (LLMs) to extract the scores from these texts as accurately as possible. The format for the response should be a number and no other text so there are two aspects to the challenge: ensuring that the LLM returns only a number, and instructing it to deduce the correct number for the text. As part of this, the LLM prompt needs to explain when to return the missing value code, -1, instead of a number when the text does not clearly contain one. The article also provides an example of a simple prompt. The purpose of the challenge is twofold: to get an effective solution to this problem, and to increase understanding of prompt design and LLM capabilities for complex numerical tasks. The initial solution suggested has an accuracy of 72.6%, so the challenge is to beat this.