---
pretty_name: LifeStreamingCoT
language:
- en
license: apache-2.0
version: "v0.4.1"
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train.parquet
  - split: test
    path: data/eval.parquet
- config_name: high_quality
  data_files:
  - split: train
    path: data/train_high_quality.parquet
  - split: test
    path: data/eval_high_quality.parquet
task_categories:
- text-generation
tags:
- streaming-reasoning
- selective-reasoning
- quality-refined
- supervised-fine-tuning
- sft
- dialogue
- task-oriented-dialogue
- life-assistant
- streamingthinker
size_categories:
- 1K<n<10K
---
# LifeStreamingCoT

Current version: v0.4.1: Loading Config and High-Quality Subset Patch

LifeStreamingCoT is a text-only, life-scenario adaptation of StreamingCoT-style data for StreamingThinker-style supervised fine-tuning. It keeps compatibility with earlier LifeStreamingCoT schemas while adding quality metadata and high-quality subset files.

## Version 0.4: Quality Refinement

v0.3 introduced selective concise streaming reasoning, semantic chunk splitting, skip labels, and chunk-level metadata. v0.4 improved quality by fixing keyword-stitching in emotional support examples, reducing daily-dialogue intent mistakes, replacing vague task-oriented updates, reducing fragment chunks, and adding `quality_score` plus `is_high_quality`.

v0.4 also provides high-quality subset files:

- `data/train_high_quality.jsonl`
- `data/eval_high_quality.jsonl`
- `data/train_high_quality.parquet`
- `data/eval_high_quality.parquet`


## Version 0.4.1: Loading Config and High-Quality Subset Patch

v0.4.1 is a patch over v0.4. It fixes Hugging Face loading behavior by adding explicit dataset card configs. The default config now points only to the full dataset files, while the `high_quality` config points only to the stricter high-quality subset files.

```python
from datasets import load_dataset

full = load_dataset("skyzhou06/LifeStreamingCoT", "default")
hq = load_dataset("skyzhou06/LifeStreamingCoT", "high_quality")
```

Expected split sizes for v0.4.1:

- `default/train`: 7457
- `default/test`: 1865
- `high_quality/train`: 2570
- `high_quality/test`: 634

The high-quality subset excludes copied-source responses, awkward answer templates, keyword-stitching, repeated context chunks, weak candidates, and severe quality flags.

## Version History

| Version | Summary |
| --- | --- |
| v0.1 | Schema-complete source-grounded baseline |
| v0.2 | More specific rule-based reasoning and quality flags |
| v0.3 | Selective concise reasoning, skip labels, semantic chunking |
| v0.4 | Quality refinement, quality scores, high-quality subset |
| v0.4.1 | HF loading config fix, stricter high-quality filtering |

## Recommended Usage

Full dataset:

```python
from datasets import load_dataset
ds = load_dataset("skyzhou06/LifeStreamingCoT")
```

Quality filtering:

```python
clean = ds.filter(lambda x: x["is_high_quality"] and x["quality_score"] >= 0.85)
```

Removing flagged data:

```python
clean = ds.filter(lambda x: len(x["quality_flags"]) == 0)
```

## Schema

- `id`
- `domain`
- `source_dataset`
- `instruction`
- `context`
- `context_chunks`
- `streaming_reasoning`
- `deep_reasoning`
- `answer`
- `response`
- `messages`
- `text`
- `num_chunks`
- `language`
- `split`
- `generation_method`
- `quality_flags`
- `version`
- `reasoning_policy`
- `chunking_method`
- `chunk_labels`
- `skip_chunks`
- `skip_reasons`
- `reasoning_token_budget`
- `original_num_chunks`
- `chunk_split_count`
- `quality_score`
- `is_high_quality`
- `refinement_method`
- `llm_augmented`
- `llm_augmentation_model`
- `rejected_reason`
- `state_tracking_confidence`

## Source Datasets

Used sources:

- `b-mc2/wikihow_lists`: 622 rows, domain `how_to_guidance`
- `pietrolesci/multiwoz_all_versions`: 2987 rows, domain `task_oriented_assistant`
- `pixelsandpointers/better_daily_dialog`: 3713 rows, domain `daily_dialogue`
- `pixelsandpointers/empathetic_dialogues_for_lm`: 2000 rows, domain `emotional_support`

Skipped sources:

- None

## Splits

- Train: 7457
- Eval: 1865
- Total: 9322
- High-quality train: 2570
- High-quality eval: 634

## Statistics

- Average chunks: 6.45
- Average chunk length: 8.95
- Average streaming reasoning words: 28.50
- Average deep reasoning words: 16.48
- Average quality score: 0.989
- High-quality percentage: 96.70%
- Skip chunk ratio: 0.1119
- LLM augmented rows: 0

## Quality Flags

- `closing_mishandled`: 7
- `copied_source_response`: 469
- `excessive_chunking`: 174
- `long_deep_reasoning`: 2
- `merged_fragments`: 5797
- `too_many_skips`: 2
- `weak_context`: 10

## Example

```json
{
  "id": "life_task_oriented_assistant_000001",
  "domain": "task_oriented_assistant",
  "source_dataset": "pietrolesci/multiwoz_all_versions",
  "instruction": "Help the user complete a real-life task based on gradually revealed information.",
  "context": "Chunk 1: I'm looking for a restaurant called the gandhi.\nChunk 2: Can you book it for 8 people on Sunday at 14:45?\nChunk 3: I would also like to find somewhere fun in the middle of town to go.\nChunk 4: Sorry, I actually wanted somewhere in the west.\nChunk 5: Sure, please provide me with the postcode and address.\nChunk 6: Thank you for your help, that is all I need today.",
  "context_chunks": [
    "I'm looking for a restaurant called the gandhi.",
    "Can you book it for 8 people on Sunday at 14:45?",
    "I would also like to find somewhere fun in the middle of town to go.",
    "Sorry, I actually wanted somewhere in the west.",
    "Sure, please provide me with the postcode and address.",
    "Thank you for your help, that is all I need today."
  ],
  "streaming_reasoning": "C1 domain=restaurant; goal=search. C2 goal=booking; time+=8, 14:45; day+=sunday; party_size+=8 people. C3 destination+=find somewhere fun in the middle of town. C4 area+=west. C5 goal=request_info; requested_info+=address, postcode. C6 [SKIP: closing_only].",
  "deep_reasoning": "Need domain=restaurant; goal=search, booking, request_info; area=west; party_size=8 people; destination=find somewhere fun in the middle of town; requested_info=address, postcode; when=sunday, 8, 14:45; closing_detected.",
  "answer": "You're welcome. Glad I could help; have a great day.",
  "response": "Streaming reasoning: C1 domain=restaurant; goal=search. C2 goal=booking; time+=8, 14:45; day+=sunday; party_size+=8 people. C3 destination+=find somewhere fun in the middle of town. C4 area+=west. C5 goal=request_info; requested_info+=address, postcode. C6 [SKIP: closing_only].\n\nDeep reasoning: Need domain=restaurant; goal=search, booking, request_info; area=west; party_size=8 people; destination=find somewhere fun in the middle of town; requested_info=address, postcode; when=sunday, 8, 14:45; closing_detected.\n\nAnswer: You're welcome. Glad I could help; have a great day.",
  "messages": [
    {
      "role": "user",
      "content": "Instruction: Help the user complete a real-life task based on gradually revealed information.\n\nContext:\nChunk 1: I'm looking for a restaurant called the gandhi.\nChunk 2: Can you book it for 8 people on Sunday at 14:45?\nChunk 3: I would also like to find somewhere fun in the middle of town to go.\nChunk 4: Sorry, I actually wanted somewhere in the west.\nChunk 5: Sure, please provide me with the postcode and address.\nChunk 6: Thank you for your help, that is all I need today."
    },
    {
      "role": "assistant",
      "content": "Streaming reasoning: C1 domain=restaurant; goal=search. C2 goal=booking; time+=8, 14:45; day+=sunday; party_size+=8 people. C3 destination+=find somewhere fun in the middle of town. C4 area+=west. C5 goal=request_info; requested_info+=address, postcode. C6 [SKIP: closing_only].\n\nDeep reasoning: Need domain=restaurant; goal=search, booking, request_info; area=west; party_size=8 people; destination=find somewhere fun in the middle of town; requested_info=address, postcode; when=sunday, 8, 14:45; closing_detected.\n\nAnswer: You're welcome. Glad I could help; have a great day."
    }
  ],
  "text": "<|user|>\nInstruction: Help the user complete a real-life task based on gradually revealed information.\n\nContext:\nChunk 1: I'm looking for a restaurant called the gandhi.\nChunk 2: Can you book it for 8 people on Sunday at 14:45?\nChunk 3: I would also like to find somewhere fun in the middle of town to go.\nChunk 4: Sorry, I actually wanted somewhere in the west.\nChunk 5: Sure, please provide me with the postcode and address.\nChunk 6: Thank you for your help, that is all I need today.\n<|assistant|>\nStreaming reasoning: C1 domain=restaurant; goal=search. C2 goal=booking; time+=8, 14:45; day+=sunday; party_size+=8 people. C3 destination+=find somewhere fun in the middle of town. C4 area+=west. C5 goal=request_info; requested_info+=address, postcode. C6 [SKIP: closing_only].\n\nDeep reasoning: Need domain=restaurant; goal=search, booking, request_info; area=west; party_size=8 people; destination=find somewhere fun in the middle of town; requested_info=address, postcode; when=sunday, 8, 14:45; closing_detected.\n\nAnswer: You're welcome. Glad I could help; have a great day.",
  "num_chunks": 6,
  "language": "en",
  "split": "train",
  "generation_method": "source_grounded_rule_based_v0.4_quality_refined",
  "quality_flags": [],
  "version": "v0.4",
  "reasoning_policy": "selective_concise",
  "chunking_method": "semantic_sentence_split_v0.4_refined",
  "chunk_labels": [
    "reason",
    "reason",
    "reason",
    "reason",
    "reason",
    "skip"
  ],
  "skip_chunks": [
    6
  ],
  "skip_reasons": {
    "6": "closing_only"
  },
  "reasoning_token_budget": {
    "streaming_reasoning_max_words_per_chunk": 18,
    "deep_reasoning_max_words": 45,
    "answer_max_sentences": 3
  },
  "original_num_chunks": 6,
  "chunk_split_count": 0,
  "quality_score": 1.0,
  "is_high_quality": true,
  "refinement_method": "rule_based_quality_refinement_v0.4",
  "llm_augmented": false,
  "llm_augmentation_model": null,
  "rejected_reason": null,
  "state_tracking_confidence": 0.99
}
```

## Limitations

- Still primarily rule-based unless optional LLM augmentation is run.
- Not expert advice.
- Some source datasets are dialogue-style and may not perfectly match assistant behavior.
- The high-quality subset is recommended for serious SFT experiments.