Natural Context Drift Undermines the Natural Language Understanding of Large Language Models (2025)

Abstract: How does the natural evolution of context paragraphs affect Question Answering (QA) in generative Large Language Models (LLMs)? To address this, we propose a framework for curating naturally evolved, human-edited variants of reading passages from contemporary QA benchmarks and for analysing LLM performance across a range of semantic similarity scores, which quantify how closely each variant aligns with Wikipedia content on the same article topic that the LLM saw during pretraining. Using this framework, we evaluate 6 QA datasets and 8 LLMs with publicly available training data. Our experiments reveal that LLM performance declines as reading passages naturally diverge from the versions encountered during pretraining–even when the question and all necessary information remains present at inference time. For instance, average accuracy on BoolQ drops by over 30% from the highest to lowest similarity bins. This finding suggests that natural text evolution may pose a significant challenge to the language understanding capabilities of fully open-source LLMs.

## introduction large language models (llms), pre-trained on massive web-scale corpora, have been shown to be highly effective at question answering (qa) over text passages @xcite @xcite , a task that has long served as a testbed for evaluating natural language understanding (nlu) @xcite . while such progress has fostered the perception that llms possess strong nlu capabilities already, much of this success may stem from shortcut exploitation rather than genuine language understanding @xcite @xcite , as revealed by robustness probing-a commonly used evaluation methodology that goes beyond surface-level performance to assess authentic comprehension. existing qa robustness evaluation paradigms typically operate statically: a perturbation function is applied to the original benchmark test set, and model performance is then measured on the resulting challenge set @xcite . differentiating from previous work, this paper offers a novel dynamic perspective on understanding the limitations of generative llms by asking: what happens when reading paragraphs continue to evolve and diverge from their appearance during pretraining? such scenarios are prevalent in real-world applications, where textual data instances can evolve over time due to ongoing human edits, content updates, or contextual shifts (e.g., wikipedia articles @xcite ), thus requiring llms to demonstrate genuine language understanding. to the best of our knowledge, however, there has been no systematic investigation of this phenomenon in qa. to address this gap, we propose a framework to analyse how llm performance changes as the reading paragraph semantically diverges from the content of its source in the model's training data. among various instances of evolving text corpora, we focus on wikipedia, as it serves as a primary source of reading passages in many widely used qa benchmark datasets @xcite @xcite @xcite , is commonly included in llms pretraining data @xcite , and, most importantly, provides clearly documented revision histories that capture natural text evolution @xcite . this allows us to curate human-edited variants of passages that reflect natural changes over time. our approach adopts a gradual perspective by computing continuous semantic similarity scores at the paragraph level and correlating them with llms qa accuracy. within the developed framework, we empirically evaluate six qa datasets and eight llms with fully open-source training corpora. our study finds that, across models with different training corpora and qa dev set open-source prediction training corpora english encyclopedic subset apc revision histories curate extract original reading passages verbatim original passage matching corresponding edited passage identification naturally evolved human-edited reading paragraphs extraction semantic similarity-llm average accuracy correlation analysis title/content t 1 c 1 ... ... tn c n wikipedia title max semantic similarity measurement with content that belongs to the same title human performance assessment across bins prediction correctness checking inference bin analysis human annotators wikipedia article titles candidate original passages candidate edited passages c_op 1 c_ep 1 … … c_op k c_ep k … … c_op n c_ep n original passage edited passage question answer op 1 ep 1 q 1 ans 1 op 2 ep 2-1 q 2 ans 2 op 2 ep 2-2 q 2 ans 2 op 2 … q 2 ans 2 op 2 ep 2-k q 2 ans 2 … … … … op n ep n q n ans n semanticsimilarity llm's prediction question only? architectural configurations, as context paragraphs naturally evolve and become semantically distant from the wikipedia content sharing the same article title seen during pretraining, the qa performance of llms generally deteriorates. in contrast, human annotators are less affected by such semantic drift and maintain relatively stable accuracy regardless of passage similarity, suggesting that the observed performance drop is specific to llms and not due to deficiencies in the edited passages themselves. ## methodology in our framework (figure 1 ), we extract revision histories of paragraphs from qa benchmarks, order them by semantic similarity to the version that appears in an llm's training corpus, and correlate the llms' answer accuracy on those passages to the similarity thus obtained. the framework consists of two modules, described in detail below. ## experiments setup broadly, we address the following question: how well do llms perform on qa as the reading paragraphs naturally evolve from the versions present in their training corpus? to this end, we select qa benchmarks that feature context paragraphs from wikipedia, whose edit histories allow us to trace naturally evolved versions of each paragraph, and evaluate llms with an open-source training corpus, as detailed below. datasets: we use the development set of six english qa datasets spanning extractive, yes/no, abstractive, cause-effect reasoning and multi-hop reasoning challenge: squad 1.1 @xcite , squad 2.0 @xcite , ad-versarialqa -d(roberta) @xcite , boolq @xcite , wikiwhy @xcite (version 1.2) and hotpotqa @xcite (in the "distractor" setting). for hot-potqa, which includes paragraphs from multiple wikipedia articles within a single context, we retain only the edits applied to the gold passages, i.e., those containing supporting facts that determine the answer, and disregard other distractors. ## results and discussion as a general trend, the qa performance of instruction-finetuned olmo llms deteriorates as the reading paragraph semantically deviates from the training corpus. this is clearly visualized in figure 2 , where, across the evaluated benchmark datasets, model average accuracy generally declines as the reading paragraphs evolve and exhibit lower semantic similarity to the versions found in the wikipedia subset of the training corpus. for example, on boolq, the accuracy of olmo-2-1124-7b-instruct declines sharply from 71.1% in the highest similarity bin 0.9-1.0 to 12.5% in the lowest 0.1-0.2. comparable drops are also observed, for instance, for olmo-7b-0724-instruct on squad 2.0 and (a) squad 2.0 (b) wikiwhy (c) hotpotqa figure 3: average accuracy of other instruction-finetuned llms across ten semantic similarity bins. wikiwhy, and for olmo-2-1124-13b-instruct on d(roberta) and hotpotqa, illustrating the broader impact of natural context drift in diverse qa challenges. to further substantiate the observed trend, we perform a slope analysis as shown in appendix d figure 5 , where linear regressions are fitted to each model's accuracy trajectory across semantic similarity bins. aggregated across all tasks and models, the mean slope is 65.78 ± 41.55, with a mean pearson correlation of 0.684 ± 0.291, highlighting a consistent and statistically grounded relationship between semantic divergence and performance degradation. further, this downward trend is not exclusive to the olmo family. as shown in figure 3 , a comparable decline in average performance across decreasing similarity ranges is likewise observed in other instruction-finetuned llms, including amberchat, tinyllama-1.1b-chat-v1.0 and dolly-v2-7b/12b, despite differences in model size, architecture, training corpora and procedure. to further investigate the generalisability of our findings, we conduct an ablation study using alter-native embedding models for semantic similarity measurement (figure 6 in appendix e), evaluating the base pre-trained versions of the instructionfinetuned llms (figures 7 and 8 in appendix f) and prompting llms to perform chain-of-thought (cot) reasoning @xcite when generating responses (figures 9 and 10 in appendix g). overall, the observed effects of context drift persist under these alternative settings, supporting the generalisability of our conclusions. we find from figures 2, 3 and 5 that the impact of natural context drift is more pronounced in benchmarks that emphasize surface-form alignment, whereas tasks requiring deeper reasoning exhibit greater resilience. the downward trend is less pronounced for datasets requiring advanced reasoning, such as wikiwhy and hotpotqa. this may be because natural text evolution introduces diverse linguistic cues and contextual variations that activate broader reasoning mechanisms in llms, partially offsetting the impact of semantic drift. in contrast, surface-level qa tasks such as squad rely more heavily on lexical or structural cues @xcite and therefore appear more sensitive to the text evolution. dataset-level statistics support this contrast: boolq and squad 2.0 exhibit quite steep average slopes (67.23 and 83.50, respectively) and strong pearson correlations between average accuracy and semantic similarity (e.g., boolq's average @xmath0.867 ± 0.064). meanwhile, hot-potqa demonstrates a much shallower 23.94 average slope and lower correlation, indicating greater robustness to textual edits. unlike llms, human performance in reading comprehension is not influenced by deviations in measured semantic similarity. to determine whether the observed decline in llms' accuracy is due to reduced semantic similarity or the possibility that the edited reading paragraphs became degraded and unanswerable, we evaluate human performance across semantic similarity bins within the four investigated datasets. for each dataset, we randomly sample an equal number of edited qa instances from each bin, assign two annotators to label the answers, and involve a third annotator to resolve any disagreements. details of the human annotation protocol are provided in appendix h. as shown in figure 4 , across all qa benchmarks, human performance does not consistently decline with decreasing semantic similarity, supporting the conclusion that the degradation in llm accuracy stems from semantic drift rather than a loss of question answerability. there may be little concern regarding the impact of paragraph leakage from other data sources beyond wikipedia. a natural question arises as to whether, given the vast scale of llms' training corpora, the edited versions of the reading paragraphs might also appear in sources beyond the wikipedia subset we focus on, potentially affecting the findings. therefore, using the infini-gram engine @xcite , we aim to estimate as accurately as possible the percentage of edited reading paragraphs that appear verbatim in the complete training corpus of the evaluated llms across the six examined qa benchmarks, as shown in ## conclusion we introduce a novel methodology for examining how real-world natural context evolution affects the language understanding of llms when it deviates semantically from their training data. leveraging wikipedia revision histories, we curate naturally human-edited variants of benchmark reading passages, compute their semantic similarity to versions present in the models' training corpus, and correlate these similarity scores with model performance. our empirical findings show that, while natural text evolution has little to no effect on human qa performance, recent fully open-source llms generally exhibit a consistent downward trend in accuracy as semantic similarity decreases. we hope this study contributes to the growing body of research focused on understanding and addressing the limitations of llms in real-world, evolving textual contexts.