Self-Rationalization in the Wild: A Large-scale Out-of-Distribution Evaluation on NLI -related tasks (2025)

Abstract: Free-text explanations are expressive and easy to understand, but many datasets lack annotated explanation data, making it challenging to train models for explainable predictions. To address this, we investigate how to use existing explanation datasets for self-rationalization and evaluate models’ out-of-distribution (OOD) performance. We fine-tune T5-Large and OLMo-7B models and assess the impact of fine-tuning data quality, the number of fine-tuning samples, and few-shot selection methods. The models are evaluated on 19 diverse OOD datasets across three tasks: natural language inference (NLI), fact-checking, and hallucination detection in abstractive summarization. For the generated explanation evaluation, we conduct a human study on 13 selected models and study its correlation with the Acceptability score (T5-11B) and three other LLM-based reference-free metrics. Human evaluation shows that the Acceptability score correlates most strongly with human judgments, demonstrating its effectiveness in evaluating free-text explanations. Our findings reveal: 1) few annotated examples effectively adapt models for OOD explanation generation; 2) compared to sample selection strategies, fine-tuning data source has a larger impact on OOD performance; and 3) models with higher label prediction accuracy tend to produce better explanations, as reflected by higher Acceptability scores.1

## introduction generating textual explanations has been a major focus in machine learning and nlp @xcite @xcite , as the explanations are expressive † most of work was done during a research stay at ukp. 1 code available at: https://github.com/ukplab /tacl2025-ood-eval-self-rationalization . and do not require readers to have model-level knowledge to understand. one popular line of work is self-rationalization @xcite , in which a model jointly generates the task label and a free-text explanation for the predicted label. compared with highlighting words and phrases @xcite , free-text explanations can express unstated knowledge and common-sense in easily understandable forms. however, datasets containing annotated free-text explanations are rare due to expensive annotations. a few datasets for free-text explanation generation exist @xcite @xcite @xcite , with e-snli @xcite being one of the seminal datasets in the nli area. based on snli @xcite , e-snli focuses on reasoning over fine-grained nuances of common-sense knowledge. however, datasets containing longer or more domain-specific text, such as fact-checking on real-world claims, lack annotated explanations @xcite . this poses severe challenges for (i) training and (ii) evaluating self-rationalizing models on these tasks. no largescale analysis-exists to understand how well selfrationalization models can transfer from existing data to unknown datasets. we fill the gap by learning self-rationalization from established sources with annotated explanations and evaluating its generalization performance on 19 out-of-distribution (ood) datasets over three related tasks (see evaluation setup in figure 1 ): nli, fact-checking (fc), and hallucination detection of abstractive summarization (hdas). nli focuses on textual entailment within a controlled context, fc extends to real-world claims with retrieved evidence, and hdas centers around machine-generated text. our ood figure 1 : ood evaluation pipeline of self-rationalization. the pipeline comprises two main parts. the first part (a) relates to learning to self-rationalize with a source dataset (section 3); it involves sample selection and fine-tuning a generative model. the second part (b) relates to ood generation and evaluation (section 4); we evaluate the model on three categories of ood tasks: nli, fact-checking, and hallucination detection of abstractive summarization. ## related work free-text explanation generation and evaluation self-rationalization has been a popular approach for generating free-text explanations @xcite @xcite @xcite . @xcite show that joint learning of label prediction and explanation generation results in explanationsmore aligned with predicted labels. @xcite addressed the scarcity of annotated explanation data by using prompt-based finetuning on a few examples, though their evaluation was limited to in-distribution datasets. few works have studied how such models can generalize to ood. @xcite studied how learning with few-shot instances with template-based explanations influences ood generalization. their ood dataset (e-hans) is limited with constructed templates based on the hans dataset @xcite . @xcite studied the effect of self-rationalization on reducing mod-els' reliance on spurious cues in out-of-domain datasets, and they showed that self-rationalization improves models robustness when fine-tuning data size is small. @xcite studied the setup where the target dataset has few annotated free-text explanations but abundant labels. their approach is limited to target datasets in which free-text explanations exist. in contrast to the above ood evaluations, we focus on the ood evaluation of self-rationalization for 19 diverse datasets, and our evaluation does not rely on reference explanations. reliable evaluation is crucial for explanation generation. traditional metrics that measure text overlap with references have shown low correlation with human judgments @xcite , and reference explanations are not always available. recent works, like tigerscore @xcite , auto-j @xcite , and themis @xcite , use llms as evaluators. these metrics rely on detailed instructions specifying evaluation aspects (e.g., relevance, accuracy, coherence) and formatted inputs for the task. the trained metric then generates a rating along with a textual analysis. to test their suitability for the explanation generated with self-rationalization, in this work, we study their correlations with human judgments. ## ood generation and evaluation this section introduces part (b) of the pipeline in figure 1 . for all fine-tuned models, we perform inference on all ood datasets. ## results and analysis this section begins with ood label prediction results. we then evaluate explanations through human judgments and analyze their correlation with reference-free metrics. next, we report explanation evaluation results across all datasets using the most correlated metric. finally, we present the overall performance of each ood dataset on the best-performing models. ## discussion this section discusses possible reasons for our earlier findings, focusing on how fine-tuning data and model influence label prediction and explanation generation. we also examine the link between label prediction performance and acceptability scores across the three ood tasks. ## conclusion this work investigated self-rationalization models' ability to generalize to nli-related ood tasks through the evaluation on 19 diverse datasets. we achieve this by fine-tuning t5-large and olmo-7b under different configurations (varying fine-tuning dataset source, size, and instance selection strategies) to study the impact of data size and quality on ood task performance and explanation quality. we also examined the acceptability score as a reference-free metric for the generated explanation evaluation through a human evaluation. through the study, we gained some important insights: i) fine-tuning a model on few-shot examples can perform surprisingly well in ood datasets compared to fine-tuning on a large full-size dataset; ii) fine-tuning data source, compared to sample selection, has a larger impact on ood performance; iii) acceptability score is positively related to models label prediction performance.