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---
pretty_name: Flight Search Intent Eval
license: mit
language:
- en
- de
- es
task_categories:
- text-generation
tags:
- eval
- benchmark
- travel
- intent-parsing
- structured-output
- multilingual
size_categories:
- n<1K
configs:
- config_name: default
data_files:
- split: en
path: en.jsonl
- split: de
path: de.jsonl
- split: es
path: es.jsonl
---
# Flight Search Intent Eval
244 real flight search queries in English, German and Spanish, each paired with the
structured search specification a model should produce from it.
This is the step _before_ booking: turning one messy sentence into origin, destination,
dates, duration, passengers, cabin and filters. That specification decides which
itineraries a traveler is ever shown, so grading it is grading whether the model
understood which flights they meant. τ-bench and similar benchmarks cover what happens
after a search spec exists. This covers what produces it.
| split | cases |
| --------- | ------- |
| `en` | 150 |
| `de` | 50 |
| `es` | 44 |
| **total** | **244** |
## Results
Current leaderboard across 9 models:
**https://fjmatrix.github.io/flight-search-intent-eval/**
## A case
```json
{
"id": "ow-europe-seasia-mar",
"lang": "en",
"text": "Europe to south east Asia mid march",
"tags": ["oneway", "fuzzy-location", "flexible-date"],
"expect": {
"action": "get_tickets",
"search_type": "oneway",
"trips": [
{
"departure": { "fuzzy": true, "names": ["Europe"] },
"arrival": { "fuzzy": true, "names": ["Southeast Asia", "South East Asia"] },
"departure_date": "03/10/26-03/20/26"
}
]
}
}
```
`text` is the query as a traveler wrote it. `expect` asserts only the fields that query
actually determines — anything it does not mention is simply absent, and no grader is
run for it.
Expected dates are written against an anchor date of **01/01/2026**, which is also the
date the model under test is told is today. Change one and you must change the other.
## Tags
| tag | cases | |
| ---------------- | ----- | -------------------------------------------------------------------- |
| `roundtrip` | 153 | out and back, stated as either a return date or a length, never both |
| `flexible-date` | 116 | a window rather than a day |
| `fuzzy-location` | 109 | a region, a country, or a vibe — no single IATA code |
| `oneway` | 59 | no return stated |
| `multi` | 12 | two or more trips, each with its own date, in ascending order |
| `invalid-date` | 7 | a date that cannot be honored — already past, or no such day |
| `invalid-others` | 3 | reads as a flight query but cannot become a search |
## Grading
Eleven graders run per case. Each returns pass, fail, or **not applicable** — and
not-applicable stays out of the denominator, so no model is rewarded or punished for a
field its case says nothing about. A case passes only when every dimension it asserts
passes.
| grader | asserts |
| --------------------------------------------- | --------------------------------------------------------------------------------------- |
| `gradeAction` `gradeSearchType` | search vs. invalid; oneway / roundtrip / multi |
| `gradeOrigin` `gradeDestination` | the right airports — or, for places with no IATA code, the right region |
| `gradeDateRange` `gradeDuration` | departure and return land inside the allowed window; the stay inside the allowed nights |
| `gradePassengers` `gradeCabin` `gradeFilters` | passengers, cabin, and the stops / price / bags / connection filters |
| `gradeDateSanity` | no departure before today, no return before departure, multi-city legs in order |
| `gradeTripShape` | trip count matches the search type; `return_date` and `duration` never both set |
The last two take no expected value. They hold for any valid search, so every case is
scored on them.
What keeps this from being brittle:
- **Dates are windows.** `"03/15/26"` demands that day. `"10/01/26-10/31/26"` accepts any
day in October, because _"somewhere warm in October"_ has no single right answer.
- **Durations are ranges.** `"7"` is exactly a week, `"3-5"` accepts 3 to 5 nights,
`"21-"` is three weeks or more.
- **Several airports can be right.** `{"any_code": ["ORD", "CHI", "MDW"]}` — Chicago is
Chicago.
- **Regions are compared by meaning.** _"west europe"_, _"beach towns in Southeast Asia"_
have no IATA code, so returned names are paired against expected ones by embedding
cosine (≥ 0.80), one-to-one and order-free.
## Running it
The harness, prompt, JSON schema and per-vendor adapters live in the GitHub repo:
**https://github.com/fjmatrix/flight-search-intent-eval**
```bash
npm install && npm run eval
```
Each run writes per-model JSON plus a self-contained HTML leaderboard.
## License
MIT.