The study examines how varying the context window size of GPT‑2 affects its alignment with human reading-time data. A U‑shaped relationship is found: very short windows (<20 tokens) capture local memory limits, while larger windows (500–1,000 tokens) provide the best overall fit. Counterfactual experiments that disrupt cross‑sentential coreference chains show that these long‑range coreference relations are crucial for the improved fit of larger context windows.
arXiv:2609.24238v1 Announce Type: new Abstract: We reproduce and stress-test the work of Yu et al. (2023), who characterize how language models (LMs) arbitrate between memorized knowledge and contrad...
STRUCTURALCOST is a self‑paced reading dataset comprising 475 participants and 40,800 observations that isolates the processing cost of long‑distance subject‑verb dependency resolution. The study replicates a known psycholinguistic finding at NLP scale: human reading times at the main verb increase with dependency length, driven by syntactic embedding beyond linear distance. Various language models—n‑gram, SSMs, and transformers—partially mirror this graded difficulty profile but consistently underestimate the integration cost humans incur, a gap that persists across architectures and model sizes.
MGAL is a new multilingual benchmark for evaluating long‑context large language models, built from United Nations reports in six official UN languages and covering 8K to 128K tokens. It tests four linguistic granularities—word, sentence, paragraph, and document—while also stratifying examples by their position within the document (begin, middle, end). Experiments show that models excel at word‑level tasks but struggle with coarser granularity, and that closed‑source models outperform others in lower‑resource languages, revealing challenges such as local semantic crowding and a fluency‑consistency gap.
arXiv:2511. 21338v2 Announce Type: replace Abstract: Masked Diffusion Language Models (MDLMs) have recently emerged as a promising alternative to Autoregressive Language Models (ARLMs), leveraging a denoising objective that, in principle, should enable more uniform context utilisation.
arXiv:2609.22101v1 Announce Type: cross Abstract: Large language models can process increasingly long prompts, yet their ability to locate and use decisive evidence may degrade as irrelevant or confu...
Contextual entrainment, which is a newly discovered phenomenon in large language models (LLMs), refers to the tendency of a model to assign higher probabilities to tokens that appear in its context. In this work, we extend this phenomenon from the token level to the sentence level by examining the per-token mean log-probability of a sentence instead of the probabilities of individual tokens.
As large language models (LLMs) grow more capable, they are increasingly deployed in context-rich settings where task inputs are often accompanied by long, partially irrelevant context. In a controlled setting, we find that state-of-the-art models often appear robust to task-irrelevant context at the aggregate level: prepending it to benchmark questions causes little change in overall accuracy.
arXiv:2608. 12218v1 Announce Type: cross Abstract: Large language models are increasingly trained and deployed with long contexts that span documents, code repositories, and interaction histories.
The study examines whether Dutch language models exhibit coherence‑illusion effects similar to human readers, using texts that refer back to earlier context with words like ‘again’ and ‘too’. Surprisal at the critical word aligns with human acceptability and eye‑tracking data, showing that models are more surprised by incoherent continuations unless a matching distractor is present. Attention entropy and an energy metric from associative‑memory literature reveal heads that behave differently under coherence versus incoherence, and ablating these heads demonstrates transfer effects across experiments, indicating a shared underlying mechanism.
arXiv:2608. 13578v1 Announce Type: cross Abstract: Transformer architectures rely on dense self-attention to model long-range dependencies, but this mechanism exhibits quadratic complexity with respect to sequence length.
arXiv:2603. 20843v3 Announce Type: replace-cross Abstract: Long-context language modeling is commonly framed as a scalability challenge of token-level attention, yet local-to-global information structuring remains largely implicit in existing approaches.
arXiv:2610.12989v1 Announce Type: new Abstract: We investigate whether language models contain internal components that contribute to representing narrativity, the degree to which a text exhibits nar...