The paper examines how Large Language Models’ internal representations become sparser as task difficulty rises, finding that harder inputs lead to more concentrated activation patterns in the last hidden state. This sparsity–difficulty relationship holds across various models and domains, indicating that models learn dense representations for familiar patterns but default to sparse activations for unfamiliar ones. Leveraging this insight, the authors propose Sparsity‑Guided Curriculum In‑Context Learning (SG‑ICL), which selects few‑shot demonstrations based on sparsity and outperforms standard CoT and Auto‑CoT baselines on the MATH‑500 benchmark.
arXiv:2605.12484v3 Announce Type: replace Abstract: Large language models (LLMs) are trained for downstream tasks by updating their parameters (e.g., via RL). However, updating model parameters force...
arXiv:2510. 01163v2 Announce Type: replace Abstract: The factors driving the performance of in-context learning (ICL) in large language models (LLMs) remain poorly understood despite ICL's surprising effectiveness, enabling models to adapt to new tasks from only a handful of examples.
arXiv:2510. 11686v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) promises to expand the capabilities of language models, but it is unclear if current RL techniques promote the discovery of novel behaviors, or simply sharpen those already present in the base model.
The paper investigates how large language models learn new tasks in-context, comparing rule-based instruction following to example-based few-shot prompting across five diverse tasks. Results show that models generally learn more reliably from rule descriptions than from examples alone, and adding more examples does not consistently improve performance. Instruction tuning further enhances rule-based learning while preserving example-based capabilities, with rule advantages being strongest for algebraic tasks and weaker for tasks requiring distributional sensitivity or parametric knowledge.
The paper introduces a family of adapters that enhance language model reasoning by adding selective state-space control at token and context levels. The token-level MaLoRA makes the adapter’s scaling factor dynamic and recurrent, improving over static low‑rank adaptation. The context-level MaRA tracks cross‑segment reasoning state and retrieves relevant segments, outperforming an eight‑billion‑parameter dense retriever and boosting reasoning accuracy by an average of +6.4 F1 over LoRA.
arXiv:2409. 02228v2 Announce Type: replace Abstract: When language models (LMs) are trained to forget (or "unlearn'') a skill, how precisely does their behavior change?
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.
arXiv:2507. 18043v2 Announce Type: replace-cross Abstract: Inference-time steering methods offer a lightweight alternative to fine-tuning large language models (LLMs) and vision-language models (VLMs) by modifying internal activations at test time without updating model weights.
arXiv:2606. 05644v1 Announce Type: new Abstract: When retrieved evidence contradicts parametric memory, language models frequently ignore context and default to memorized priors -- a failure that undermines the core purpose of retrieval augmentation.
arXiv:2610.27724v1 Announce Type: new Abstract: Dense linear probes assess task readability from language model (LM) representations, but do not directly reveal how many features suffice for predicti...
The paper investigates which linguistic operations can be replaced by latent task representations instead of in‑context prompts. A 2.6 M‑parameter network learns to encode the geometry of a few‑shot support set and applies an additive update to a frozen GPT‑2 model, achieving zero‑shot performance comparable to or better than 10‑shot in‑context learning for forward inflection and lemmatization. However, for arbitrary pairings such as antonymy, the latent approach only reaches about half the performance of in‑context learning, indicating that productive rules amortize well while memorized pairings do not.
The paper investigates whether few-shot in-context learning (ICL) emerges similarly across different data modalities. Using a controlled cross-modality framework, the authors test the Convergent Emergence Hypothesis, which posits that tasks benefiting from ICL in one modality will also benefit in others. They find that paired-mapping ICL appears in six modalities—language, genome, integer sequences, time series, images, and proteins—outperforming baselines and showing correlated task effects in five of them, supporting the hypothesis in some but not all cases.