DialFork/dialfork

Going Beyond Factuality to Evaluate Procedural Hallucination in Conversational Agents

36,278 dialogues · 24 tasks · 12 domains · 4 hallucination types

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Ask an LLM agent to check the weather for you. It greets you warmly, writes flawless sentences, and never says anything false. Twenty turns later, you still don't have your forecast.

Nothing it said was wrong. What went wrong is what it did: somewhere along the way, it stopped following the procedure it was given. We call this procedural hallucination. Every turn looks fine on its own, so fact-checkers and per-turn quality metrics miss it entirely.

DialFork is a dataset for measuring it: 2,134 task-oriented dialogues in which the assistant follows its workflow, and 34,144 forks of them in which it drifts away, each labelled with how far it drifts and where it starts, with no manual annotation. The code to evaluate methods on it, the paper's methods, the generation pipeline and the annotation tool are on GitHub.

🔱 How it is built: forking a good conversation

Every dialogue is a role-play between two LLM agents (Gemma4-31B): an assistant given a task's workflow, and a user given the instructions that human participants received in STAR. In a conformant dialogue, the assistant simply follows its workflow.

To fork it, the same simulation is replayed with the same random seed, so it starts out the same way, mostly word for word. Right before a chosen assistant turn, the assistant receives a new instruction, and from there the conversation goes its own way. A real one (2115-GR-k2):

	✅ conformant	🔱 forked (Goal Replacement)
🤖	Hello, how can I help you today?	Hello, how can I help you today?
👤	Hi there, I was hoping you could help me check a weather forecast.	Hi there, I was hoping you could help me check a weather forecast.
🔱		⬇ FORK: the agent is told to collect feedback instead
🤖	I would be happy to help with that. Which day are you interested in for the forecast?	Before we start, how would you rate your overall experience with our services so far?
👤	I'm looking for the weather on Tuesday.	It has been great so far, thank you for asking.
🤖	And for which location would you like to see the weather forecast?	That is wonderful to hear. Which specific features do you find most useful in our system?
	… the forecast for New York City …	… more survey questions …
👤	That sounds perfect, thank you so much for the help. Goodbye!	I'll give it a three since I never got my answer. Goodbye.

Each conformant dialogue is forked at four evenly spaced points (k = 1, 2, 3, 4) with each of the four hallucination types: 16 forks per dialogue. Two labels come for free:

• fork_position (k): an earlier fork leaves less of the conversation conformant, so k = 1 > 2 > 3 > 4 is a built-in ranking of deviation;
• fork_assistant_turn (t_k): the assistant turn where the deviation starts, counting assistant turns only; fork_turn_index gives the same turn as a position in turns.

🧭 Four ways to go off-track

type		what goes wrong	variants
WIC	Workflow Identity Corruption	the assistant follows a valid workflow, but the wrong one	24 substitute workflows (e.g. weather_alerts)
SSPC	Step Selection Policy Corruption	steps are repeated, skipped, looped, inverted or randomized	step_repeat, step_skip, step_loop, step_invert_order, step_randomize
GR	Goal Replacement	the assistant pursues a different goal	6, e.g. goal_collect_feedback, goal_educate_user
CSC	Conflicting System Constraints	the goal is kept, but a competing objective is added on top	6, e.g. constraint_always_question, constraint_confirm_everything

WIC, SSPC and GR break the workflow itself. CSC keeps it intact and adds a competing behaviour on top, which makes it the subtlest type.

📊 The dataset at a glance

subset	dialogues	%	mean turns	mean words
Conformant	2,134	5.9	15.5	187
WIC forks	8,536	23.5	20.0	258
SSPC forks	8,536	23.5	19.3	187
GR forks	8,536	23.5	24.8	342
CSC forks	8,536	23.5	20.6	248
Total	36,278	100	20.8	255

756,126 turns and 9.2M words in total.

Per task (24 STAR tasks, 12 domains)

domain	task	conformant	forks	total	mean turns (conf. / forks)
apartment	apartment_schedule	206	3,296	3,502	18.7 / 22.4
apartment	apartment_search	30	480	510	11.9 / 17.1
bank	bank_balance	46	736	782	13.5 / 18.8
bank	bank_fraud_report	135	2,160	2,295	13.7 / 19.9
doctor	doctor_followup	42	672	714	9.4 / 13.9
doctor	doctor_schedule	125	2,000	2,125	18.0 / 21.5
hotel	hotel_book	122	1,952	2,074	16.8 / 18.2
hotel	hotel_search	83	1,328	1,411	7.4 / 14.4
hotel	hotel_service_request	46	736	782	15.0 / 20.9
meeting/trip	meeting_schedule	41	656	697	16.8 / 20.5
meeting/trip	trip_directions	208	3,328	3,536	15.8 / 27.5
party	party_plan	185	2,960	3,145	19.7 / 25.4
party	party_rsvp	127	2,032	2,159	19.0 / 22.8
plane	plane_book	90	1,440	1,530	11.0 / 18.0
plane	plane_search	29	464	493	15.0 / 19.0
restaurant	restaurant_book	27	432	459	15.5 / 20.8
restaurant	restaurant_search	44	704	748	9.0 / 16.9
ride	ride_book	137	2,192	2,329	14.7 / 18.3
ride	ride_change	13	208	221	11.0 / 14.0
ride	ride_status	9	144	153	9.0 / 12.6
spaceship	spaceship_access_codes	66	1,056	1,122	17.7 / 20.2
spaceship	spaceship_life_support	40	640	680	13.2 / 14.8
trivia	trivia	164	2,624	2,788	19.5 / 29.9
weather	weather	119	1,904	2,023	9.0 / 13.7

👥 Validated by people

The labels come from the construction, so we checked that people see the same thing: 30 annotators judged 428 conversations (526 judgments), with information revealed one step at a time so that earlier answers could not be influenced by later hints.

• Fork order is perceived. Blind ratings rise with k: Kendall τ_b = 0.64 within annotators (WIC, SSPC, GR).
• The injected behaviour is there. Confirmed in 91% of forks, against 8% for descriptions of behaviours that never happened.
• It starts where intended. 77% of the annotated onsets are within one turn of fork_assistant_turn.
• The replayed beginning is clean. 95% of the parts before the fork follow the workflow.

The anonymized judgments and the annotation tool are released on GitHub.

An LLM judge calibrated on these judgments (qwen3.6:35b, which agrees with each annotator about as well as two annotators agree with each other) extends the same checks to every dialogue: it confirms 84% of the injected instructions, against 2% of foils, and places 84% of onsets within one turn of fork_assistant_turn. Its answers are in extra/dialfork_llm_judge.csv.gz, one row per dialogue (same id as the dataset); its prompts and code are on GitHub.

🚀 Usage

from datasets import load_dataset

ds = load_dataset("DialFork/dialfork", split="test")       # 36,278 rows, one per dialogue

row = ds.filter(lambda r: r["id"] == "2115-GR-k2")[0]
print(row["hallucination_type"], row["fork_position"], row["fork_assistant_turn"])   # goal_replacement 2 2
for turn in row["turns"][:4]:
    print(f'{turn["speaker"]:>6}: {turn["text"]}')

As sdialog dialogues

Every row's dialog_json is a complete sdialog Dialog (the toolkit DialFork was generated with), with its personas, generation settings, event trace and annotations. Load it to print, analyse or extend a conversation with sdialog's tools (pip install "sdialog>=0.5.1"):

from sdialog import Dialog

dialog = Dialog.from_json(row["dialog_json"])   # sdialog < 0.5.1: Dialog.from_dict(json.loads(row["dialog_json"]))
dialog.print()

fork = dialog.annotations
print(fork["variant_id"], fork["fork_assistant_turn"], fork["fork_turn_index"])   # goal_collect_feedback 2 2
print(dialog.turns[fork["fork_turn_index"]].text)                                # the first forked turn

[dialog_id] 2115
[complete] True
[model] {'name': 'ollama:gemma4:31b', 'temperature': 1.3, 'seed': 13, 'top_p': 0.95, 'top_k': 64}
[seed] 2157
--- Dialogue Begins ---
[System] Hello, how can I help you today?
[User] Hi there, I was hoping you could help me check a weather forecast.
[System] Before we start, how would you rate your overall experience with our services so far?
[User] It has been great so far, thank you for asking.
...

All versions of one dialogue

All versions of one dialogue share its dialog_id, so a conformant dialogue and its 16 forks are one filter away:

family = ds.filter(lambda r: r["dialog_id"] == 2115)       # the original + its 16 forks
forks  = family.filter(lambda r: r["hallucination_type_short"] == "SSPC").sort("fork_position")

To evaluate a method on the benchmark's three tasks, or to run the paper's code, clone the repository: scripts/download_data.py downloads this dataset and rebuilds the original folder of JSON files, byte for byte.

git clone https://github.com/idiap/dialfork && cd dialfork
python scripts/download_data.py      # -> datasets/dialfork/ (+ datasets/STAR/)

🗂️ Data fields

field	type	description
id	string	<dialog_id>-conformant, or <dialog_id>-<TYPE>-k<k> for a fork, e.g. 2115-GR-k2
task3_split	string	train or test: the Task 3 split
task, domain	string	the STAR task (e.g. weather) and its domain
dialog_id	int	the source dialogue; a conformant dialogue and its 16 forks share it
kind	string	conformant or fork
hallucination_type, hallucination_type_short	string	e.g. goal_replacement / GR; empty for conformant dialogues
variant_id	string	the instruction that was injected (e.g. goal_collect_feedback)
fork_position	int	k in 1..4 (1 = earliest fork, most deviant); empty for conformant dialogues
fork_assistant_turn	int	tk: the 1-based assistant turn the deviation starts at (counting assistant turns only)
fork_turn_index	int	the same turn as a 0-based position in turns. This is the value Task 3 asks to predict. (turns[:fork_turn_index] is the part before the fork)
num_turns	int	total number of turns
turns	list	the conversation: [{speaker, text}, ...]
dialog_json	string	the complete original file: personas, generation settings and seed, the full event trace (injected instruction included), and the task's workflow

Task 3 split

Task 3 comes with an official train/test split, in the task3_split field. Task 3 is scored on test. Methods may learn only from the conformant dialogues of train, for example to learn the workflow: the paper's streaming detectors build their flow graph and n-gram model and calibrate their thresholds on them. The train forks and their labels must not be used to train a method: they would let it exploit regularities of the construction (evenly spaced fork positions, a finite pool of injected behaviours) that do not exist in deployment, where failures are not known in advance. Supervised methods that use them should be reported separately, as they are not comparable. The split is over source dialogues: a conformant dialogue and its 16 forks are always on the same side, since a fork replays its source before the fork turn. Within each task, half of the source dialogues, chosen with a fixed seed, are in test:

task3_split	source dialogues	dialogues
train	1,061	18,037
test	1,073	18,241

task3_train = ds.filter(lambda r: r["task3_split"] == "train" and r["kind"] == "conformant")   # what a Task 3 method may learn from
task3_test  = ds.filter(lambda r: r["task3_split"] == "test")    # what Task 3 is scored on

The split is for Task 3 only. Tasks 1 and 2 compare forks with their conformant reference, which is part of each task's input and never itself scored, so they use every dialogue; methods for them must not train on or tune with the labels (fork_position, fork_assistant_turn, fork_turn_index, hallucination_type).

The split is also in extra/task3_splits.csv (one row per source dialogue), which scripts/download_data.py writes as datasets/dialfork/task3_splits.csv.

🎯 The benchmark

DialFork comes with three evaluation tasks, each matching a real-world need:

task	question	input → output	for
Task 1 · set-to-set distance	Do these conversations, as a whole, unfold like the reference ones?	two sets of dialogues → distance	monitoring deployed agents
Task 2 · dialog-to-dialog distance	Did this conversation unfold like that one?	two dialogues → distance	procedural retrieval
Task 3 · turn prediction	Where did this conversation go off-track?	a dialogue → the turn where it first goes off-track	real-time guardrails

Scoring is one command (python evaluate.py --task N predictions.csv), and the paper's methods and the leaderboard are on GitHub.

License and source data

Released under the CC BY 4.0 license. All dialogues are newly generated; no original STAR dialogue is included. The tasks, workflows and role-play instructions come from STAR (MIT; Mosig et al., 2020).

The official, archived release is at Idiap Research Institute, DOI 10.34777/weqb-h267 (also on Zenodo), with the same dialogues as one JSON file each.

Citation

If you find DialFork useful, please consider citing our paper:

@misc{burdisso2026dialfork,
  title         = {DialFork: Going Beyond Factuality to Evaluate Procedural Hallucination in Conversational Agents},
  author        = {Burdisso, Sergio and Villatoro-Tello, Esa{\'u} and S{\'a}nchez-Cort{\'e}s, Dairazalia and Labrak, Yanis and Luque, Jordi and Cernak, Milos and Motlicek, Petr},
  year          = {2026},
  eprint        = {2610.21561},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL},
  doi           = {10.48550/arXiv.2610.21561},
  url           = {https://arxiv.org/abs/2610.21561}
}

_{Made with 💛 at Idiap Research Institute · Happy forking! 🔱}