# Neural Models and Language Model Prompting for the Multidimensional Evaluation of Open-Ended Conversations

Source: [Neural Models and Language Model Prompting for the Multidimensional Evaluation of Open-Ended Conversations](<https://www.sota2.com/research/paper/68fa7b0e1e209131cea866f7>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-03-31T05:00:04.048Z

Authors: Michelle Elizabeth, Alicja Kasicka, Natalia Krawczyk, Magalie Ochs, Gw\\'enol\\'e Lecorv\\'e, Justyna Gromada, Lina M. Rojas-Barahona

Published: 2025-08-31T13:24:05.000Z

## Abstract

The growing number of generative AI-based dialogue systems has made their evaluation a crucial challenge. This paper presents our contribution to this important problem through the Dialogue System Technology Challenge (DSTC-12, Track 1), where we developed models to predict dialogue-level, dimension-specific scores. Given the constraint of using relatively small models (i.e. fewer than 13 billion parameters) our work follows two main strategies: employing Language Models (LMs) as evaluators through prompting, and training encoder-based classification and regression models. Our results show that while LM prompting achieves only modest correlations with human judgments, it still ranks second on the test set, outperformed only by the baseline. The regression and classification models, with significantly fewer parameters, demonstrate high correlation for some dimensions on the validation set. Although their performance decreases on the test set, it is important to note that the test set contains annotations with significantly different score ranges for some of the dimensions with respect to the train and validation sets.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Dialogue response evaluation on DSTC 12 (test)](<https://www.sota2.com/research/sota/dialogue-response-evaluation-on-dstc-12-test.md>): Dialogue response evaluation; DSTC 12 (test); Empathy: 0.17.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2509.00841>)

[PDF](<https://arxiv.org/pdf/2509.00841.pdf>)