Contribution to the handbook
Set aside · Assessed 2026-10-28
The requested details concern a particular study benchmark. The supplied packet gives no such settings or hardware, and the available BERT context does not establish a specific design implication for implementing city- or country-scale governance.
Does this challenge the goal?
None identified
The question asks for experimental training details and hardware and does not contest the handbook’s goal.
The supplied evidence describes BERT model and embedding context but does not provide the requested training settings or hardware.
Why this category?
- study-specific technical detail
- no demonstrated system-level design implication
The missing requested details in this packet are not evidence that the full paper lacks them.
This is an AI assessment of contribution to the goal, separate from whether the question has an answer.
New evidence is being assessed. Any answer below is provisional; parts whose evidence changed or was withdrawn are withheld.
State the reported training passes, learning rate, weight decay, and computing hardware and memory.
What we can say so far
The supplied evidence does not report BERT’s training passes, learning rate, weight decay, computing hardware, or memory, so those settings cannot be stated from this material.
What remains open
- Reported number of training passes.
- Reported learning rate.
- Reported weight decay.
- Reported computing hardware and memory.
Conditions and limits
- The evidence describes interpretations of the retrieved preprint; figures were not independently interpreted.
Evidence and related ideas
Supporting evidence, qualifications and context play different roles in this answer.
context
Models in the comparison The comparison includes six language models and BERT SQuAD, a simpler benchmark. The models differ in what is known about their training: GPT 3.5 Turbo has proprietary architecture and data; LLaMA-2, LLaMA-3, Mistral, and Gemma are open-weight models trained on proprietary data; Falcon is open-weight and trained on public data; and BERT is a foundation model trained on public data.
Identifies BERT as a model in the comparison, but does not report its training settings or hardware.
Large Language Models (LLMs) as Agents for Augmented Democracy
Read full source: Large Language Models (LLMs) as Agents for Augmented Democracy (opens in a new tab) ↗Read source discussion: Large Language Models (LLMs) as Agents for Augmented Democracy →
context
Preparing BERT prompts and embeddings The researchers translated prompts into Brazilian Portuguese using a prompt provided in Appendix C. They said BERT’s predictive accuracy depends heavily on the quality of its embeddings, which they use to support contextual understanding. They therefore used BERTimbau embeddings, which they report were derived from brWaC, a Portuguese text collection with 3.53 million documents and 2.68 billion tokens.
Reports BERT’s embeddings and their stated source, but not the requested training settings or hardware.
Large Language Models (LLMs) as Agents for Augmented Democracy
Read full source: Large Language Models (LLMs) as Agents for Augmented Democracy (opens in a new tab) ↗Read source discussion: Large Language Models (LLMs) as Agents for Augmented Democracy →