Task-Level Natural Language Priors as Learning Signals for Low-Resource LLM Training proposes Prior-Guided Tuning (PGT), a training approach that treats natural-language priors as auxiliary learning signals rather than just input context. The method introduces Contrastive Prior Steering (CPS), which adds positive and negative prior-conditioned auxiliary losses while preserving the original supervised objective. Experiments on AmbiMath, Jigsaw, and MNLI/HANS demonstrate that CPS consistently outperforms plain and prompt fine-tuning, achieving high accuracy and significant gains with limited training data.
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.
The paper introduces CIG‑SFT, a supervised fine‑tuning method that uses conditional information gain to evaluate how much each demonstrated token reduces uncertainty about the task. By comparing token likelihoods from a frozen reference model with and without task input, the method derives a KL‑regularized projection of the current policy, yielding a scalar‑weighted SFT objective whose gradients match the projected target. Experiments on mathematical reasoning and code‑generation benchmarks show that CIG‑SFT outperforms standard SFT and adaptive fine‑tuning baselines, especially in generalizing to challenging evaluation distributions, without requiring extra data or online rollouts.
The paper introduces SFT-as-context, a training‑free method that lets a parent large language model (LLM) use the response of a supervised fine‑tuned (SFT) model as context to answer queries. This approach preserves the parent model’s general capabilities while acquiring the SFT model’s specialized skills, achieving performance close to the SFT model on fine‑tuned benchmarks and near the parent model on general tasks. Experiments across 19 parent‑SFT pairs and 11 benchmarks show minimal gaps in performance, and the method can solve problems requiring both specialized and general knowledge, even when neither model alone succeeds.
arXiv:2606. 29407v1 Announce Type: cross Abstract: There has been increasing interest in exploring the capabilities of advanced large language models (LLMs) in the field of information extraction (IE), specifically focusing on tasks related to named entity recognition (NER) and relation extraction (RE).
arXiv:2606. 12117v1 Announce Type: cross Abstract: Benchmark scores often misrepresent a large language model's (LLM's) knowledge, because they rely, e.
arXiv:2606. 01304v1 Announce Type: new Abstract: Hard negative mining has become the dominant strategy for training retrievers, yet it faces intrinsic limitations: negatives are bounded by corpus availability, selected by retriever score rather than diagnostic value, and increasingly contaminated by false positives as the retriever improves.
arXiv:2505.23323v3 Announce Type: replace Abstract: This position paper argues that many behaviours framed as either "stochastic parroting" or "advanced reasoning" are better explained as context-dir...
arXiv:2610.14025v1 Announce Type: cross Abstract: Activation steering controls large language model behavior by adding a direction to hidden activations during inference. For steering to work well, t...
arXiv:2603. 19294v4 Announce Type: replace Abstract: While post-training has successfully improved large language models (LLMs) across a variety of domains, these gains heavily rely on human-labeled data or external verifiers.
QVAC Genesis III is a 191.43 B‑token synthetic STEM corpus covering 19 domains and multiple difficulty levels, created through a dual generation strategy that uses a weak edge‑scale student model to generate corrective explanations and contrastive reasoning. The authors evaluate the corpus with an LLM‑as‑a‑parser protocol and demonstrate that 1.7 B‑parameter models trained on QVAC Genesis III outperform those trained on Cosmopedia‑v2 and the Cosmo‑1B model on ARC, GPQA Diamond, and MMLU STEM benchmarks, achieving up to +28.57% improvement on ARC‑E and a 99.45% valid answer rate.
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:2607. 06160v1 Announce Type: cross Abstract: Synthesizing long-context supervised fine-tuning (SFT) data is a scalable way to enhance the long-context understanding of large language models (LLMs), yet existing approaches share three limitations: narrow task coverage, insufficient instruction difficulty, and a lack of faithfulness supervision.