arXiv:2601. 22546v2 Announce Type: replace-cross Abstract: The recent advancements in Large Language Models (LLMs) have attracted interest in exploring their in-context learning abilities and chain-of-thought capabilities.
arXiv:2508. 10875v3 Announce Type: replace-cross Abstract: Diffusion Language Models (DLMs) are rapidly emerging as a powerful and promising alternative to the dominant autoregressive (AR) paradigm.
The paper presents a benchmark that compares seven long‑form generation frameworks across three granularities—single chapter, multi‑chapter, and whole book—using an anchor‑based LLM‑as‑a‑judge protocol to evaluate outlines directly. Results show no single framework dominates across all settings; performance depends on how well a framework’s output form matches the target granularity, with SuperWriter excelling in length‑constrained single‑chapter mode but losing advantage in whole‑book mode. The study finds only moderate correlation between outline and writing quality, supporting the idea that these two stages should be evaluated separately.
While large language models have been dominating the research landscape recently, small language models remain highly relevant across various domains; yet, they receive far less attention. In this study, we investigate how smaller language models perform during the generation stage within a Retrieval-Augmented Generation (RAG) system.
arXiv:2607. 08284v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated rapidly improving long-context capabilities, prompting a wave of benchmarks designed to evaluate them.
FLEET is a new method for text generation that adds a memory mechanism to large language models. It represents each generation as a sparse trajectory of high‑entropy states and uses these trajectories to compute per‑token utility scores that adjust the logits. Benchmarks show that FLEET matches the accuracy of repeated sampling while being three times faster and improving accuracy on complex coding tasks, all with minimal changes to existing pipelines.
arXiv:2606. 30062v1 Announce Type: cross Abstract: While large language models have been dominating the research landscape recently, small language models remain highly relevant across various domains; yet, they receive far less attention.
arXiv:2601.03199v2 Announce Type: replace-cross Abstract: Diffusion language models (DLMs) have shown strong potential for general natural language tasks with in-context examples. Existing In-Context...
The paper introduces LLM-Microscope, a toolkit for measuring how Large Language Models encode contextual information at the token level. It shows that seemingly minor tokens—such as determiners, stopwords, and punctuation—carry surprisingly high contextual weight, and removing them degrades performance on benchmarks like MMLU and BABILong-4k. The study also finds a strong link between contextualization and linearity, indicating that the transformation between layers can be approximated by a single linear mapping when tokens are well contextualized.
arXiv:2608.29921v1 Announce Type: cross Abstract: The output of a Language Model can be tampered with \emph{while} the model is writing it. A simple test can thus be constructed by evaluating the mod...
The paper introduces the first large‑scale benchmark dataset of Bangla idioms, along with a synthetic multiple‑choice question set for idiom meaning identification. It evaluates recent large language models on three idiom‑related tasks—paraphrasing, idiom span detection, and meaning identification—using zero‑shot and few‑shot prompting. Results show significant variability across models, with Phi‑4‑mini‑instruct best at paraphrasing, Kimi‑K2‑32b‑instruct excelling at span detection, and Gemini‑2.5‑flash leading in meaning identification.
arXiv:2609.34187v2 Announce Type: replace-cross Abstract: The strong version of the stochastic parrot argument claims that, although large language models (LLMs) may exceed rote regurgitation, they c...
Large language models (LLMs) have demonstrated rapidly improving long-context capabilities, prompting a wave of benchmarks designed to evaluate them. However, existing long-context evaluations - from Needle-in-a-Haystack (NIAH) tests to more recent multi-hop reasoning and summarization tasks - predominantly measure average-case performance, and many are either saturated or lack robustness.