# Overview of Dialog System Evaluation Track: Dimensionality, Language, Culture and Safety at DSTC 12

arXiv: https://arxiv.org/abs/2509.13569 · HTML: https://mlsanity.com/paper/2509.13569/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 23 references that are in the map.
- First posted on arXiv 16 Sep 2025.

## Builds on (most cited first, 12 of 12)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Build it Break it Fix it for Dialogue Safety: Robustness from Adversarial Human Attack](https://mlsanity.com/paper/1908.06083/) (2019)
- [Unsupervised Evaluation of Interactive Dialog with DialoGPT](https://mlsanity.com/paper/2006.12719/) (2020)
- [ProsocialDialog: A Prosocial Backbone for Conversational Agents](https://mlsanity.com/paper/2205.12688/) (2022)
- [EuroLLM: Multilingual Language Models for Europe](https://mlsanity.com/paper/2409.16235/) (2024)
- [CoSafe: Evaluating Large Language Model Safety in Multi-Turn Dialogue Coreference](https://mlsanity.com/paper/2406.17626/) (2024)
- [Automatic Evaluation and Moderation of Open-domain Dialogue Systems](https://mlsanity.com/paper/2111.02110/) (2021)
- [What is wrong with you?: Leveraging User Sentiment for Automatic Dialog Evaluation](https://mlsanity.com/paper/2203.13927/) (2022)
- [Overview of Robust and Multilingual Automatic Evaluation Metrics for Open-Domain Dialogue Systems at DSTC 11 Track 4](https://mlsanity.com/paper/2306.12794/) (2023)
- [On the Benchmarking of LLMs for Open-Domain Dialogue Evaluation](https://mlsanity.com/paper/2407.03841/) (2024)
- [Soda-Eval: Open-Domain Dialogue Evaluation in the age of LLMs](https://mlsanity.com/paper/2408.10902/) (2024)
- [Interactive Evaluation of Dialog Track at DSTC9](https://mlsanity.com/paper/2207.14403/) (2022)

## Built on by (most cited first, 1 of 1)

- [CATCH: A Controllable Theme Detection Framework with Contextualized Clustering and Hierarchical Generation](https://mlsanity.com/paper/2512.21715/) (2025)

## Abstract (arXiv)

The rapid advancement of Large Language Models (LLMs) has intensified the need for robust dialogue system evaluation, yet comprehensive assessment remains challenging. Traditional metrics often prove insufficient, and safety considerations are frequently narrowly defined or culturally biased. The DSTC12 Track 1, "Dialog System Evaluation: Dimensionality, Language, Culture and Safety," is part of the ongoing effort to address these critical gaps. The track comprised two subtasks: (1) Dialogue-level, Multi-dimensional Automatic Evaluation Metrics, and (2) Multilingual and Multicultural Safety Detection. For Task 1, focused on 10 dialogue dimensions, a Llama-3-8B baseline achieved the highest average Spearman's correlation (0.1681), indicating substantial room for improvement. In Task 2, while participating teams significantly outperformed a Llama-Guard-3-1B baseline on the multilingual safety subset (top ROC-AUC 0.9648), the baseline proved superior on the cultural subset (0.5126 ROC-AUC), highlighting critical needs in culturally-aware safety. This paper describes the datasets and baselines provided to participants, as well as submission evaluation results for each of the two proposed subtasks.