mistral-24b-instruct-ft-synthetic
LoRA adapter accompanying an anonymous conference submission on counsellor-side
language models. It is one cell of a 2x2 matrix per model family: the pretrained
base and its instruction-tuned release, each with and without conversation-preserving
fine-tuning. Only the fine-tuned cells need an adapter — base and instruct
are the unmodified public checkpoints.
- Base model:
mistralai/Mistral-Small-24B-Instruct-2501
- Code: https://anonymous.4open.science/r/aiu-audit-0797
- Objective: conversation-preserving supervised fine-tuning. Client turns stay
visible in the context, only counsellor turns contribute to the loss.
- LoRA: r=64, alpha=128, dropout=0.05, three epochs, all attention and MLP
projections.
Training data
Fine-tuned on the negative-control pool, 14,935 rows capped at 2,540 per corpus (MI Dataset 2,235).
| Corpus | Setting | Source |
|---|
| ESConv | crowd-sourced peer support | Liu et al., ACL 2021, 10.18653/v1/2021.acl-long.269 |
| MI Dataset | Reddit motivational-interviewing forum | — |
| ExTES | model-generated emotional support | Zheng et al. 2023, ailover/ExTES |
| PsyDial (zh) | model-generated counselling dialogue | qiuhuachuan |
| SoulChat (zh) | peer replies augmented with model output | Chen et al., Findings of EMNLP 2023 |
| MentalChat16K | synthetic dialogue with anonymised clinical interview transcripts | Xu et al. 2025, ShenLab/MentalChat16K |
No reply in this pool was written by a licensed professional, which is the
property the control isolates.
Data governance
No restricted material was used in training. The held-out reference of real
counselling correspondence that the submission evaluates against is in no
training pool, and no output conditioned on it is released.
Intended use
Research on counselling register only. Not for clinical use, not for deployment
facing help-seekers, supervised or otherwise.
