README.md

metadata

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.

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:

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

Quality filtering:

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

Removing flagged data:

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

{
  "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.