arXiv AI
Human Values in a Single Sentence: Moral Presence, Hierarchies, and Transformer Ensembles on the Schwartz Continuum
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arXiv AI
Jul 17
Introspection Fine-Tuning (IFT): Training Small LLMs to Introspect
arXiv:2607. 14111v1 Announce Type: cross Abstract: Can small language models detect and report on perturbations their own internal activations?
arXiv AI
Jun 24
SURGELLM: Rethinking Multi-Task Evaluation through Task-Aware Feature Gating with Class-Balanced Normalization
arXiv:2606. 24259v1 Announce Type: cross Abstract: Fine-tuned encoders deployed across heterogeneous NLP tasks face three compounding problems: mismatched inductive biases, class-imbalance corruption of feature statistics, and no mechanism to condition attention on external lexical knowledge.
arXiv AI
Jun 9
SLMJury: Can Small Language Models Judge as Well as Large Ones?
arXiv:2606. 07810v1 Announce Type: cross Abstract: Large language models (LLMs) are widely used as judges for evaluating model outputs, but their high cost, latency, and opacity limit scalability.
arXiv AI
Jul 7
Detecting Hallucinations in Retrieval-Augmented Generation through Grounding-Aware Sensitivity by Perturbation (GASP)
arXiv:2607. 04223v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) reduces but does not eliminate hallucination, and existing detectors return a single answer-level score that does not indicate which sentence is unsupported, or why.
arXiv Computation and Language
Sep 1
Manac\'a-1B: An Open, Reproducible Brazilian-Portuguese Language Model and a Tokenizer-Aware, Paired Evaluation
Manacá-1B is a 1.72‑billion‑parameter, open decoder‑only language model trained from scratch for Brazilian Portuguese, released with a fully containerized, reproducible training pipeline and complete logs. The authors evaluate it against nine open baselines on four Portuguese benchmarks, reporting standard errors and paired significance tests, and find that Manacá-1B outperforms smaller models on LAMBADA‑PT while remaining competitive on commonsense completion. They also uncover a tokenizer‑related evaluation pitfall that can drastically lower accuracy and provide a simple fix, releasing all code, logs, and corrected tokenizer for full reproducibility.
arXiv AI
5d ago
LLMs or Naive Bayes? Old Gems or New Ways
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.