Spurious Correlations in Reference-Free Evaluation of Text Generation
- URL: http://arxiv.org/abs/2204.09890v1
- Date: Thu, 21 Apr 2022 05:32:38 GMT
- Title: Spurious Correlations in Reference-Free Evaluation of Text Generation
- Authors: Esin Durmus, Faisal Ladhak, Tatsunori Hashimoto
- Abstract summary: We show that reference-free evaluation metrics of summarization and dialog generation may be relying on spurious correlations with measures such as word overlap, perplexity, and length.
We demonstrate that these errors can be mitigated by explicitly designing evaluation metrics to avoid spurious features in reference-free evaluation.
- Score: 35.80256755393739
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Model-based, reference-free evaluation metrics have been proposed as a fast
and cost-effective approach to evaluate Natural Language Generation (NLG)
systems. Despite promising recent results, we find evidence that reference-free
evaluation metrics of summarization and dialog generation may be relying on
spurious correlations with measures such as word overlap, perplexity, and
length. We further observe that for text summarization, these metrics have high
error rates when ranking current state-of-the-art abstractive summarization
systems. We demonstrate that these errors can be mitigated by explicitly
designing evaluation metrics to avoid spurious features in reference-free
evaluation.
Related papers
- Mitigating the Impact of Reference Quality on Evaluation of Summarization Systems with Reference-Free Metrics [4.881135687863645]
We introduce a reference-free metric that correlates well with human evaluated relevance, while being very cheap to compute.
We show that this metric can also be used alongside reference-based metrics to improve their robustness in low quality reference settings.
arXiv Detail & Related papers (2024-10-08T11:09:25Z) - Using Similarity to Evaluate Factual Consistency in Summaries [2.7595794227140056]
Abstractive summarisers generate fluent summaries, but the factuality of the generated text is not guaranteed.
We propose a new zero-shot factuality evaluation metric, Sentence-BERTScore (SBERTScore), which compares sentences between the summary and the source document.
Our experiments indicate that each technique has different strengths, with SBERTScore particularly effective in identifying correct summaries.
arXiv Detail & Related papers (2024-09-23T15:02:38Z) - FENICE: Factuality Evaluation of summarization based on Natural language Inference and Claim Extraction [85.26780391682894]
We propose Factuality Evaluation of summarization based on Natural language Inference and Claim Extraction (FENICE)
FENICE leverages an NLI-based alignment between information in the source document and a set of atomic facts, referred to as claims, extracted from the summary.
Our metric sets a new state of the art on AGGREFACT, the de-facto benchmark for factuality evaluation.
arXiv Detail & Related papers (2024-03-04T17:57:18Z) - Cobra Effect in Reference-Free Image Captioning Metrics [58.438648377314436]
A proliferation of reference-free methods, leveraging visual-language pre-trained models (VLMs), has emerged.
In this paper, we study if there are any deficiencies in reference-free metrics.
We employ GPT-4V as an evaluative tool to assess generated sentences and the result reveals that our approach achieves state-of-the-art (SOTA) performance.
arXiv Detail & Related papers (2024-02-18T12:36:23Z) - AMRFact: Enhancing Summarization Factuality Evaluation with AMR-Driven Negative Samples Generation [57.8363998797433]
We propose AMRFact, a framework that generates perturbed summaries using Abstract Meaning Representations (AMRs)
Our approach parses factually consistent summaries into AMR graphs and injects controlled factual inconsistencies to create negative examples, allowing for coherent factually inconsistent summaries to be generated with high error-type coverage.
arXiv Detail & Related papers (2023-11-16T02:56:29Z) - SWING: Balancing Coverage and Faithfulness for Dialogue Summarization [67.76393867114923]
We propose to utilize natural language inference (NLI) models to improve coverage while avoiding factual inconsistencies.
We use NLI to compute fine-grained training signals to encourage the model to generate content in the reference summaries that have not been covered.
Experiments on the DialogSum and SAMSum datasets confirm the effectiveness of the proposed approach.
arXiv Detail & Related papers (2023-01-25T09:33:11Z) - On the Limitations of Reference-Free Evaluations of Generated Text [64.81682222169113]
We show that reference-free metrics are inherently biased and limited in their ability to evaluate generated text.
We argue that they should not be used to measure progress on tasks like machine translation or summarization.
arXiv Detail & Related papers (2022-10-22T22:12:06Z) - TRUE: Re-evaluating Factual Consistency Evaluation [29.888885917330327]
We introduce TRUE: a comprehensive study of factual consistency metrics on a standardized collection of existing texts from diverse tasks.
Our standardization enables an example-level meta-evaluation protocol that is more actionable and interpretable than previously reported correlations.
Across diverse state-of-the-art metrics and 11 datasets we find that large-scale NLI and question generation-and-answering-based approaches achieve strong and complementary results.
arXiv Detail & Related papers (2022-04-11T10:14:35Z) - REAM$\sharp$: An Enhancement Approach to Reference-based Evaluation
Metrics for Open-domain Dialog Generation [63.46331073232526]
We present an enhancement approach to Reference-based EvAluation Metrics for open-domain dialogue systems.
A prediction model is designed to estimate the reliability of the given reference set.
We show how its predicted results can be helpful to augment the reference set, and thus improve the reliability of the metric.
arXiv Detail & Related papers (2021-05-30T10:04:13Z) - Understanding Factuality in Abstractive Summarization with FRANK: A
Benchmark for Factuality Metrics [17.677637487977208]
Modern summarization models generate highly fluent but often factually unreliable outputs.
Due to the lack of common benchmarks, metrics attempting to measure the factuality of automatically generated summaries cannot be compared.
We devise a typology of factual errors and use it to collect human annotations of generated summaries from state-of-the-art summarization systems.
arXiv Detail & Related papers (2021-04-27T17:28:07Z)
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.