Contribution to the handbook
Supporting · Assessed 2026-10-28
The distinction between reportedly public fine-tuned models and code that was only planned for release is useful context when evaluating whether study results can be scrutinized or reproduced. It concerns this study’s materials, however, rather than a demonstrated governance-system mechanism or a general access policy.
Does this challenge the goal?
In-scope gap
Research-material availability can inform scrutiny and testing of evidence within the existing goal; the question does not challenge that goal or its scope.
The packet reports the authors’ stated availability at the time of the paper and does not establish later access or the completeness of materials.
Why this category?
- Background relevant to scrutiny and reproducibility of study evidence
The records do not confirm whether the code was later posted. This is one study’s reported arrangement and does not establish what access a governance system should require.
This is an AI assessment of contribution to the goal, separate from whether the question has an answer.
Tests, stop conditions and unresolved work →
Read the exploration goalDistinguish the reported availability of the fine-tuned models from the stated plan to post the code, without treating planned access as confirmed access.
What we can say so far
The authors reported that the fine-tuned models were publicly available. They said the code for reproducing the results was to be posted; that plan does not confirm that the code was later posted.
What remains open
- The supplied evidence does not establish whether the code was posted after the recorded version.
Conditions and limits
- This reports what the authors said in the retrieved preprint; it is not independent verification of repository contents or later access.
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.
Describes the study’s models and their training backgrounds, but does not address access to the code or fine-tuned models.
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 →
supports
Code and model availability The appendix says the code for reproducing the results was to be posted on GitHub, while the fine-tuned models were publicly available there.
Reports publicly available fine-tuned models and code that was to be posted, while noting that later code availability is unconfirmed.
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 →
supports
Models and code access The researchers say their fine-tuned models are publicly available, while the code for reproducing the results was to be posted later.
Also distinguishes reportedly available fine-tuned models from code intended for later posting, and says eventual release is not established.
Large Language Models (LLMs) as Agents for Augmented Democracy