The paper introduces Adjacent Possible Exploration (APE), a selective fine‑tuning strategy for large language models that evaluates multiple parameter updates on small data subsets and accepts only those that surpass a performance threshold. APE draws on evolutionary optimization ideas to filter out destabilizing changes, contrasting with conventional single‑gradient fine‑tuning. Experiments on news summarization show a 33.9% BLEU gain and a 36.2% perplexity drop while keeping computational demands low.
arXiv:2503. 02368v4 Announce Type: replace-cross Abstract: While guided decoding, especially value-guided methods, has emerged as a cost-effective alternative for controlling language model outputs without re-training models, its effectiveness is limited by the accuracy of the value function.
arXiv:2608. 03655v1 Announce Type: cross Abstract: Abstractive summarization models remain vulnerable to factual inconsistency, redundancy, and weak length control.
arXiv:2602. 09492v2 Announce Type: replace-cross Abstract: Low-rank adaptation (LoRA) is a standard approach for fine-tuning large language models, yet its many variants report conflicting empirical gains, often on the same benchmarks.
RangeSWA is a training‑free method designed to improve length extrapolation in continuous diffusion language models. It combines local sliding‑window attention, remote block summaries, and dynamic content retrieval to maintain local detail while accessing long‑range context. The approach also includes positional remapping, conditioning attention correction, and denoising‑stage‑adaptive window scheduling to mitigate generation degradation, resulting in lower perplexity and fewer repetitions in long‑text generation.
arXiv:2611.06371v1 Announce Type: new Abstract: Summarization with a fixed character length is important for tasks such as headline and article preview generation, where text must fit an exact space....
arXiv:2606. 05494v1 Announce Type: cross Abstract: Automatic text summarization has become increasingly important due to the rapid growth of digital textual information.
arXiv:2607. 25675v1 Announce Type: new Abstract: Text-space optimization adapts large language models (LLMs) by editing external natural-language artifacts rather than model weights, so the optimized artifacts remain inspectable and the model can be treated as a black box.
arXiv:2606. 01252v1 Announce Type: cross Abstract: Multi-target cross-lingual text summarization (MTXLS), which summarizes a source document into multiple target languages, is increasingly important as users consume content in diverse languages, but remains underexplored.
arXiv:2609.35868v1 Announce Type: new Abstract: Is human readability necessary for effective fine-tuning of large language models? We investigate whether model-conditioned training representations ca...
The paper introduces CARPAS, a new task that dynamically refines user-provided aspects for aspect-based summarization in large language models (LLMs). It presents three new datasets and evaluates four prompting strategies, finding that LLMs tend to over-generate aspects, leading to overly long and misaligned summaries. To address this, the authors propose a two-stage framework that first generates lightweight scope guidance before aspect refinement and summarization, which improves focus, reduces over-generation, and enhances performance across all datasets.
arXiv:2507. 09839v2 Announce Type: replace Abstract: An increasing number of NLP applications interact with large language models (LLMs) through black-box APIs, making prompt engineering critical for controlling model behavior.
The paper investigates whether adding Abstract Meaning Representation (AMR) data to large language models (LLMs) improves performance on downstream tasks. By reproducing recent studies and applying a consistent hyperparameter protocol, the authors find that text-only baselines match or surpass AMR-augmented models. A perplexity-based probe shows that AMR does not provide LLMs with additional relational knowledge, suggesting no clear benefit from AMR augmentation.