arXiv:2607. 27421v1 Announce Type: cross Abstract: Intent classification is a core component of task-oriented dialogue systems, yet practitioners have limited systematic guidance for selecting deployable open-weight language models under compute, latency, and robustness constraints.
By Parishruthi Ganesh, Gerry Dozier, Cheryl Seals
DeepOpen is a decision‑oriented intent‑classification framework tailored for the BANKING77 and CLINC150 datasets. On BANKING77, a single English classifier trained with sharpness‑aware minimization (SAM) achieved a test accuracy of 94.1234% (2,899/3,080), the highest among three fixed SAM seeds. For CLINC150, a probability fusion of one Laya specialist and three DeBERTa‑v3‑large classifiers reached 98.0222% accuracy (4,411/4,500). The study reports strong task‑specific performance, notes selection effects, and highlights unresolved historical execution‑path differences rather than a new algorithmic advantage.
By Shuang Song, Muhammad Luqman Naseem, Zhongqiao Yuan
arXiv:2610.33813v1 Announce Type: new
Abstract: Fine-tuning a language model for classification involves many design decisions. Comparisons of low-rank adaptation (LoRA) with full fine-tuning are com...
By John Alderete, Srirakshith Betageri, Jennifer Flynn, Alex Greenhalgh, Josh Rubin, Yoshita Varma Annam
Large language models (LLMs) achieve strong relation extraction (RE), but their computational demands and reliance on proprietary APIs limit deployment in resource-constrained or privacy-sensitive settings. We investigate how far small language models (SLMs) can close this gap across general-domain and literary text.
The paper compares Complement Naive Bayes (NB) with zero‑shot and few‑shot large language models (LLMs) across a wide range of model sizes and text classification tasks. NB outperforms LLMs when labeled data is available, achieving comparable accuracy to large LLMs while running thousands of samples per second on a CPU. In zero‑data sentiment settings, LLMs still dominate, but NB remains the best choice for resource‑constrained HPC practitioners, and the authors provide a Kubernetes Helm operator to automate model selection.
By Mohammad Firas Sada, Dmitry Mishin, John Graham, Seungmin Kim, Mahidhar Tatineni, Frank W\"urthwein
The paper evaluates large language models (LLMs) as data quality annotators on two e-commerce tasks: entity matching and brand mislabeling. In entity matching, a simple rule-based baseline matched the LLM’s zero-shot performance (F1≈0.95), and a few-shot prompt actually lowered performance, highlighting the risk of small-sample prompt tuning. For brand mislabeling, the LLM outperformed a naive rule baseline (F1 0.833 vs 0.721) by leveraging background knowledge, and demonstrated high consistency across repeated runs (99.7% agreement).
By Praphulla Lal Shrestha