cyf32768/PREFMINE-15K

Dataset Description

PREFMINE-15K is a supervised fine-tuning dataset for identifying request-relevant user preferences from prior interaction history. Each example consists of a history of previous interaction sessions, a current user request, and the user's most likely preference for the current interaction.

The dataset is designed to train models to infer preferences that are both supported by the interaction history and relevant to the current request. These inferred preferences can then be used to guide downstream LLMs in generating more personalized responses, with the goal of improving communication efficiency and overall user experience.

The prompt template used in PREFMINE-15K is adapted from the CUPID benchmark for preference inference from prior user interactions.

Data construction pipeline: PREFMINE

Model: PREFINFER

Dataset Format

Field	Description
user_prompt_template	Template of the user prompt.
interaction_log	History of prior interaction sessions.
curr_request	Current user request.
analysis	Analysis of which prior sessions are relevant to the current request.
preference	Request-relevant preference.

Each sample consists of a system_prompt, a user_prompt, and an assistant response.

• The user_prompt contains the interaction_log and the curr_request.
• The assistant response contains the analysis and the inferred preference.

To load the dataset into a format that can be used directly for training with LLaMA-Factory, please refer to training/dataloader.py.

Prior Interaction Format

The prior_interaction field contains a sequence of previous interaction sessions between the user and an AI assistant. Each session is represented as an ordered dialogue and is formatted into a Markdown-style interaction log. Sessions are separated by horizontal delimiters, while each message is explicitly labeled according to its speaker.

For example, a prior interaction history containing two sessions is formatted as:

### Session 1

#### User

Can you summarize this article for me?

#### AI Assistant

Sure. Please provide the article you would like me to summarize.

---

### Session 2

#### User

I prefer summaries that are concise and organized into bullet points.

#### AI Assistant

Understood. I will keep future summaries concise and use bullet points when appropriate.

---

This formatted interaction log is inserted into the {interaction_log} placeholder of the user prompt. Together with the current request, it provides the evidence from which the model infers the most likely request-relevant user preference.

Training Configuration

We fine-tune Qwen3-4B-Instruct-2507 using LLaMA-Factory with LoRA.

The resulting model adapter and usage instructions are available at PREFINFER.

Key settings:

• 4 × NVIDIA A800 GPUs
• LoRA rank: 8
• LoRA alpha: 16
• LoRA dropout: 0.05
• Per-device batch size: 1
• Gradient accumulation steps: 2
• Effective batch size: 8
• Learning rate: 5e-6
• Epochs: 2
• Maximum sequence length: 131072
• DeepSpeed ZeRO-2
• FlashAttention-2
• BF16
• Gradient checkpointing

The full LLaMA-Factory configuration is provided in: training/sft_lora.yaml.

Length-aware data ordering

The training examples exhibit a highly skewed sequence-length distribution:

• approximately 14,000 examples contain at most 8,192 tokens;
• approximately 500 examples contain at most 65,536 tokens;
• approximately 500 examples contain at most 131,072 tokens.

To improve multi-GPU training efficiency, we pre-sort the training data by sequence length so that examples with similar lengths are grouped into the same effective batch whenever possible.

With 4 GPUs, a per-device batch size of 1, and gradient accumulation of 2, the effective batch size is 8. We therefore disable dataset shuffling during training to preserve the precomputed length-aware ordering.

This avoids placing extremely long and short examples in the same batch, which can otherwise cause substantial padding/computation imbalance and reduce distributed training throughput.

License

PREFMINE-15K is released under the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license.

The dataset is constructed from transformed, sampled, composed, and LLM-rewritten materials derived from multiple upstream datasets, including:

• Infinity-Instruct, released under CC BY-SA 4.0.
• WildChat-4.8M, released under the Open Data Commons Attribution License (ODC-BY).

Individual examples in PREFMINE-15K may incorporate transformed or combined materials originating from multiple upstream sources and therefore cannot necessarily be attributed to a single source dataset.

The original upstream materials remain subject to their respective licenses and applicable terms of use. Users of PREFMINE-15K are responsible for complying with the attribution, notice, and other requirements associated with the relevant upstream datasets.

The CC BY-SA 4.0 license applied to PREFMINE-15K does not supersede or replace the licenses of the upstream datasets.