Lost in Inference: Rediscovering the Role of Natural Language Inference for Large Language Models (2025)

Abstract: In the recent past, a popular way of evaluating natural language understanding (NLU), was to consider a model’s ability to perform natural language inference (NLI) tasks. In this paper, we investigate if NLI tasks, that are rarely used for LLM evaluation, can still be informative for evaluating LLMs. Focusing on five different NLI benchmarks across six models of different scales, we investigate if they are able to discriminate models of different size and quality and how their accuracies develop during training. Furthermore, we investigate the extent to which the softmax distributions of models align with human distributions in cases where statements are ambiguous or vague. Overall, our results paint a positive picture for the NLI tasks: we find that they are able to discriminate well between models at various stages of training, yet are not (all) saturated. Furthermore, we find that while the similarity of model distributions with human label distributions increases with scale, it is still much higher than the similarity between two populations of humans, making it a potentially interesting statistic to consider.

## introduction before the state-of-the-art (sota) in nlp was constituted almost exclusively by large language models (llms), a popular way of evaluating models' understanding of natural language was to consider their ability to perform natural language inference (nli) tasks (most famously, @xcite . motivated by the idea that concepts such as entailment and contradiction are central to many aspects of language meaning @xcite , in nli tasks, a model is asked to judge the relationship between the meaning of two sentences, typically chosing between entailment, contradiction, and no relationship. included in the then widely-used natural language understanding benchmark glue @xcite , the nli benchmark multi-genre natural language inference @xcite was up until relatively recently one of the most popular benchmarks to evaluate language models, and is -with over 600 citations to date in 2024 -well-cited even in the recent past. however, with the arrival of llms, mnli and other datasets have lost their spot on the sota leaderboards. with the exception of @xcite , who reported low scores for gpt-3 on the nli benchmark adversarial nli (anli, @xcite , not a single llm release paper considers an nli benchmark in their evaluation suite. 1 in this paper, we investigate why. are nli benchmarks simply not suitable to evaluate modern-day llms? are their examples too difficult or too easy? are their scores not informative? or do they, in fact, still provide a useful signal? focusing on five different nli benchmarks across six different models, we first show that they provide signal and are able to discriminate models of different size and quality ( § 4.1), but that one or more few-shot examples are needed to obtain a somewhat reasonable accuracy for models of any size. we furthermore show that performance on the datasets develop steadily during training, albeit with some fluctuations, making the benchmarks suitable for monitoring training progress ( § 4.2). for some of the benchmarks, accuracies of the best models are reaching 80-90%, for anli @xcite , however, the accuracy of even the best model does not exceed 70%. furthermore, we show that high scores are not caused by data contamination ( § 4.3). next, we investigate the extent to which improvement on the higher-scored benchmarks is still possible ( § 4.4). in a manual analysis, we find that, for the best performing model, examples with 'incorrect' predictions are rarely in fact incorrect; most concern questions on which humans may disagree as well. motivated by this result, we further explore this topic by considering the chaosnli benchmark @xcite , which contains 100 human annotations for over 4500 samples for three of the benchmarks we consider ( § 4.5). we find that accuracies (as computed on the majority label) are higher if the entropy of the human labels is low; when humans disagree, models are more likely to select one of the less preferred labels. lastly, we consider the distributional differences between model outputs and human labels, as measured by the jensen shannon divergence (jsd) between the human label distributions and the models' softmax distributions over the possible answers. we find that the jsds are lower than the ones reported by @xcite for the previous generation of models, and they are also better than chance distributions. however, they are still substantially higher than the jsd between two populations of humans foot_2 , bearing interesting implications on the viablity of using an ensemble of llm judges as a 'jury' (e.g. @xcite . interestingly, contrary to the findings of @xcite , we observe an effect of scale and model quality: jsd shows a clear decrease during training, and larger models have lower jsd than smaller models. in sum, we find that nli benchmarks are still useful for model development and improvement. specifically, they are able to discriminate between models of different scale and quality, develop steadily during training, and are not completely saturated. furthermore, as even the best models are still far away from human performance in this respect, we see promise in monitoring the development of the distributional differences between models and humans, both during and after training. ## related work before presenting our analysis, we first discuss nli or recognising textual entailment (rte) tasks ( § 2.1) and touch upon the related topic of subjectivity for nlp tasks ( § 2.2) before presenting our analysis. ## setup before reporting our results, we first describe the benchmarks and models we consider in our experiments and describe our evaluation procedure. ## results we now present our results, focusing in particular on whether nli benchmarks provide a discriminative signal for fully trained out models ( § 4.1), how their performance develops during training ( § 4.2), and what is the impact of evaluation data contamination ( § 4.3). next, we investigate whether there is still room for improvement ( § 4.4) and how model judgements compare to human judgements for ambiguous or vague questions ( § 4.5). ## limitations one of the limitation of our work is that all our analysis is limited to pre-trained models. posttraining with supervised fine-tuning and rlhf might change the behaviour of models on nli benchmarks. but, we do not believe this to be a major limitation as @xcite show that it's possible to improve the performance of models during the post-training phase, rather than lose it because of instruction fine-tuning and safety. therefore, we believe that nli benchmarks would be informative for post-trained models as well. another limitation is that we only consider the choice-based setup for evaluation. using a generative setup might change model behaviour and correspondingly, the signal provided by nli benchmarks. we leave this to future work. benchmark # samples # labels anli 1200 3 hans 30000 2 mnli 9815 3 snli 9842 3 αnli 1532 2