---
license: cc-by-4.0
language:
  - ko
task_categories:
  - text-generation
tags:
  - function-calling
  - tool-use
  - agentic
  - multi-turn
  - synthetic
  - conversational
  - korean
size_categories:
  - 10K<n<100K
pretty_name: kotraj
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*.parquet
---

# kotraj — Korean Tool-use Trajectories

**kotraj** is a synthetic dataset of **Korean-native multi-turn tool-use trajectories**, generated end-to-end in Korean — task design, user utterances, tool calls, and responses — rather than translated from English corpora. Trajectories are grounded in deterministic mock tool environments that model everyday Korean services (shopping, transport and travel booking, calendar and email), and every tool call in the released data was actually executed and replay-verified against those environments.

## Highlights

- **10,684 trajectories** across 3 domains and 30 tools defined with Korean-language JSON Schemas (OpenAI function-calling compatible)
- **Two quality tiers**: `verified` (7,010 — passed the full rule gate *and* an LLM-judge review, avg. judge score 4.91/5) and `unverified` (3,674 — clean conversations that did not fully complete the task; filter by `verify_status`)
- **Execution-grounded verification**: JSON-Schema validation of every call, `tool_call_id` consistency checks, deterministic replay of the entire call log, and machine-checkable success criteria (required calls, ordering constraints, final environment state)
- **Rich interaction mix**: multi-turn information seeking (hidden user constraints revealed only when asked), sequential dependent calls, parallel calls, single calls, and refusal/no-call cases
- **Persona-conditioned users**: 10 persona axes (age group, formality, speech style, regional context, life stage, digital literacy, request relationship, priorities, speech habits, accessibility considerations) to secure diversity under a single-teacher setup

## Dataset at a glance

| | total | verified | unverified |
|---|---|---|---|
| trajectories | **10,684** | 7,010 | 3,674 |
| avg. user turns | 3.7 | 3.5 | — |
| avg. tool calls | 4.5 | 4.0 | — |
| multi-turn ratio (≥2 turns) | 89.2% | — | — |

| call pattern | total | verified |
|---|---|---|
| multi_turn (info elicitation across turns) | 3,720 | 2,169 |
| sequential (dependent call chains) | 3,560 | 2,331 |
| single (one-shot call) | 1,527 | 1,247 |
| parallel (independent calls at once) | 1,175 | 850 |
| no_call (refusal / out-of-scope) | 702 | 413 |

| domain | trajectories | tools |
|---|---|---|
| calendar (calendar & email) | 3,747 | 10 |
| travel (transport & lodging booking) | 3,581 | 10 |
| commerce (shopping & delivery) | 3,356 | 10 |

## Example

A `verified` sequential trajectory from the `travel` domain (Korean, as released):

```
U: 아들아, 내가 예전에 잡아둔 기차표 있잖아. 예약 번호가 R0001인데, 이게 정확히
   언제 떠나는지 한번 봐주라. 손주들 데리고 갈라니까 시간이 맞나 궁금하네.
A: [tool_call] get_reservation({"reservation_id": "R0001"})
T: {"ok": true, "reservation": {"trip": {"kind": "KTX", "origin": "서울",
   "destination": "부산", "date": "2026-08-09", "departure": "09:20", ...}}}
A: 예약 번호 R0001 조회 결과 알려드릴게요!
   - 출발일: 2026년 8월 9일 (일요일)  - 출발 시간: 오전 9시 20분
   - 노선: 서울 → 부산 (KTX)  - 탑승 인원: 2명 ...
```

## Schema

| column | type | description |
|---|---|---|
| `id` | string | Unique trajectory ID |
| `domain` | string | `commerce` \| `travel` \| `calendar` |
| `tools` | string (JSON) | Full tool inventory of the domain — OpenAI function-calling compatible JSON Schemas with Korean descriptions |
| `messages` | string (JSON) | Array of `system` / `user` / `assistant` / `tool` messages; assistant messages may carry `tool_calls` |
| `n_turns` | int | Number of user turns |
| `n_tool_calls` | int | Number of tool calls |
| `call_pattern` | string | `single` \| `sequential` \| `parallel` \| `multi_turn` \| `no_call` |
| `tool_env` | string | `mock` (deterministic execution) — this release is 100% mock-executed |
| `success_criteria` | string (JSON) | Machine-checkable success spec: `required_calls` (tool, argument subset, prerequisite ordering) plus free-form `notes` for the judge |
| `judge_score` | float | Mean of 3-axis LLM-judge scores (goal / consistency / naturalness, 1–5 each); may be null for `unverified` rows |
| `verify_status` | string | **`verified`** \| **`unverified`** (see below) |
| `gen_model` | string | `upstage-family` |
| `pipeline_ver` | string | Generation pipeline version |
| `seed` | int | Mock environment seed (for reproduction) |

### Quality tiers

- **`verified`** — passed every gate: (1) the rule gate — tool schema validity, `tool_call_id` consistency, **deterministic replay of the full call log against the mock environment**, and satisfaction of `success_criteria` (required calls, call ordering, final environment state); and (2) an LLM judge scoring goal achievement, step consistency, and Korean naturalness on 1–5 scales with a threshold-based accept.
- **`unverified`** — conversations with no formatting corruption or misbehavior whose task was nevertheless left incomplete (e.g., some required calls missing). Released with labels preserved so they can be used for filtering experiments or training on imperfect interactions. For plain SFT we recommend using the `verified` tier only.

Trajectories with formatting corruption (leaked tool-call tokens), write-calls in no-call cases, turn-cap truncation, or replay mismatches were excluded from the release entirely.

## How it was built

A single-teacher, 3-role simulation pipeline:

1. **Task synthesis** — task specs are generated by combining domain × scenario category × Korean everyday situation × case variant × persona (10 axes). Every task carries machine-checkable `success_criteria` and `hidden_info` — key parameters the simulated user reveals only when asked, which forces genuine multi-turn interaction.
2. **3-role rollout** — separate user-simulator / agent (native function calling) / tool-environment roles. Tool responses come from **seed-fixed, stateful Python mock executors** (carts, reservation slots, calendars, pre-seeded resources), not from an LLM.
3. **Verification** — only trajectories that pass the rule gate (schema, consistency, deterministic replay, success criteria) are scored by an LLM judge on goal / consistency / naturalness (1–5), with threshold-based acceptance.
4. **Refinement** — two-level deduplication (task spec and trajectory signature), tier labeling, and packaging.

Generation, user simulation, and judging all use an **Upstage-family LLM** (single teacher). Diversity is obtained from the combinatorial persona/scenario/case axes rather than from multiple teacher models.

## Usage

```python
from datasets import load_dataset
import json

ds = load_dataset("drlee1/kotraj", split="train")

# Recommended for SFT: keep the verified tier only
verified = ds.filter(lambda x: x["verify_status"] == "verified")

row = verified[0]
messages = json.loads(row["messages"])
tools = json.loads(row["tools"]) 
```

## Limitations

- **Single teacher**: generation, user simulation, and judging share one Upstage-family model — style bias and self-judging limitations apply
- **Mock environments**: execution verification is exact, but response diversity and edge cases are simpler than real production APIs; some tools (e.g., product search) use literal matching without the resilience of a real search engine
- **Domain coverage**: v0.1 covers 3 domains / 30 tools; public-sector, finance, healthcare booking and other domains are planned for future versions
- **LLM judge**: `judge_score` comes from an LLM and may differ from human evaluation
- All persons, contacts, products, and reservations are fictional; the dataset contains no real personal information

## License

CC-BY-4.0

## Citation

```bibtex
@misc{kotraj2026,
  title   = {kotraj: Korean-native Multi-turn Tool-use Trajectories with Execution-grounded Verification},
  author  = {Lee, Dongryeol},
  year    = {2026},
  url     = {https://huggingface.co/datasets/drlee1/kotraj}
}
```