A Comparative Study of Controlled Text Generation Systems Using Level-Playing-Field Evaluation Principles
- URL: http://arxiv.org/abs/2605.12395v1
- Date: Tue, 12 May 2026 16:57:53 GMT
- Title: A Comparative Study of Controlled Text Generation Systems Using Level-Playing-Field Evaluation Principles
- Abstract summary: We use a level-playing-field (LPF) approach to comparative evaluation.<n>We apply a shared set of evaluation methods and datasets, selected based on those currently in use, in order to ensure fair evaluation.<n>Results: When re-evaluated in this way, performance results for a representative set of current CTG systems differ substantially from originally reported results, in most cases for the worse.
- Score: 5.999912718983889
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Background: Many different approaches to controlled text generation (CTG) have been proposed over recent years, but it is difficult to get a clear picture of which approach performs best, because different datasets and evaluation methods are used in each case to assess the control achieved. Objectives: Our aim in the work reported in this paper is to develop an approach to evaluation that enables us to comparatively evaluate different CTG systems in a manner that is both informative and fair to the individual systems. Methods: We use a level-playing-field (LPF) approach to comparative evaluation where we (i) generate and process all system outputs in a standardised way, and (ii) apply a shared set of evaluation methods and datasets, selected based on those currently in use, in order to ensure fair evaluation. Results: When re-evaluated in this way, performance results for a representative set of current CTG systems differ substantially from originally reported results, in most cases for the worse. This highlights the importance of a shared standardised way of assessing controlled generation. Conclusions: The discrepancies revealed by LPF evaluation demonstrate the urgent need for standardised, reproducible evaluation practices in CTG. Our results suggest that without such practices, published performance claims may substantially misrepresent true system capabilities.
Related papers
- Revisiting OmniAnomaly for Anomaly Detection: performance metrics and comparison with PCA-based models [0.08155575318208629]
This work revisits OmniAnomaly, a widely used recurrent model for MTSAD, and compares it with a simple linear baseline based on Principal Component Analysis.<n>Both methods are evaluated under thresholding and evaluation procedures, with experiments repeated across 100 runs for each of the 28 machines in the dataset.<n>The results show large variability across machines and show that can achieve performance comparable to OmniAnomaly, and even outperform it when point-adjustment is not applied.
arXiv Detail & Related papers (2026-03-19T14:54:38Z) - AllSummedUp: un framework open-source pour comparer les metriques d'evaluation de resume [2.2153783542347805]
This paper investigates challenges in automatic text summarization evaluation.<n>Based on experiments conducted across six representative metrics, we highlight significant discrepancies between reported performances in the literature and those observed in our experimental setting.<n>We introduce a unified, open-source framework, applied to the SummEval dataset and designed to support fair and transparent comparison of evaluation metrics.
arXiv Detail & Related papers (2025-08-29T08:05:00Z) - Practical Improvements of A/B Testing with Off-Policy Estimation [51.25970890274447]
We introduce a family of unbiased off-policy estimators that achieves lower variance than the standard approach.<n>Our theoretical analysis and experimental results validate the effectiveness and practicality of the proposed method.
arXiv Detail & Related papers (2025-06-12T13:11:01Z) - OmniEval: An Omnidirectional and Automatic RAG Evaluation Benchmark in Financial Domain [62.89809156574998]
We introduce an omnidirectional and automatic RAG benchmark, OmniEval, in the financial domain.<n>Our benchmark is characterized by its multi-dimensional evaluation framework.<n>Our experiments demonstrate the comprehensiveness of OmniEval, which includes extensive test datasets.
arXiv Detail & Related papers (2024-12-17T15:38:42Z) - Active Evaluation Acquisition for Efficient LLM Benchmarking [18.85604491151409]
We investigate strategies to improve evaluation efficiency by selecting a subset of examples from each benchmark using a learned policy.
Our approach models the dependencies across test examples, allowing accurate prediction of the evaluation outcomes for the remaining examples.
Empirical results demonstrate that our approach significantly reduces the number of evaluation prompts required.
arXiv Detail & Related papers (2024-10-08T12:08:46Z) - RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework [66.93260816493553]
This paper introduces RAGEval, a framework designed to assess RAG systems across diverse scenarios.<n>With a focus on factual accuracy, we propose three novel metrics: Completeness, Hallucination, and Irrelevance.<n> Experimental results show that RAGEval outperforms zero-shot and one-shot methods in terms of clarity, safety, conformity, and richness of generated samples.
arXiv Detail & Related papers (2024-08-02T13:35:11Z) - Better Understanding Differences in Attribution Methods via Systematic Evaluations [57.35035463793008]
Post-hoc attribution methods have been proposed to identify image regions most influential to the models' decisions.
We propose three novel evaluation schemes to more reliably measure the faithfulness of those methods.
We use these evaluation schemes to study strengths and shortcomings of some widely used attribution methods over a wide range of models.
arXiv Detail & Related papers (2023-03-21T14:24:58Z) - Towards Better Understanding Attribution Methods [77.1487219861185]
Post-hoc attribution methods have been proposed to identify image regions most influential to the models' decisions.
We propose three novel evaluation schemes to more reliably measure the faithfulness of those methods.
We also propose a post-processing smoothing step that significantly improves the performance of some attribution methods.
arXiv Detail & Related papers (2022-05-20T20:50:17Z) - PONE: A Novel Automatic Evaluation Metric for Open-Domain Generative
Dialogue Systems [48.99561874529323]
There are three kinds of automatic methods to evaluate the open-domain generative dialogue systems.
Due to the lack of systematic comparison, it is not clear which kind of metrics are more effective.
We propose a novel and feasible learning-based metric that can significantly improve the correlation with human judgments.
arXiv Detail & Related papers (2020-04-06T04:36:33Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.