Datasets:

fjmatrix
/

flight-search-intent-eval

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

{
    "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.

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

npm install && npm run eval

Each run writes per-model JSON plus a self-contained HTML leaderboard.

License

MIT.