Datasets:

ajayvikram
/

user-simulation-midtraining

# Source attribution and terms

This release reformats existing human-authored conversations into participant
perspectives. The changes are role assignment, group-preserving train/validation/test
splits, explicit source instruction extraction, and the source-specific filtering
listed below. Target utterance wording is retained, except that DeliData export
quoting is decoded: matching outer single quotes are removed and doubled apostrophes
within those wrapped strings become single apostrophes. INSPIRED also decodes its
literal QUOTATION_MARK token to a double quote. Other retained text is unchanged.
Human-authored scripted material
is included; AI messages can appear as context but are not training targets.
Each source retains its applicable terms. No blanket license is assigned by this
collection. The source pages below provide the original releases, credits, and
citation details. License identifiers are given only where explicitly documented.
| Dataset value (prefix `convokit/`) | Source and attribution | Terms / processing |
| --- | --- | --- |
| `contextual-abuse` | [Contextual Abuse Dataset](https://convokit.cornell.edu/documentation/cad.html), Vidgen et al.; ConvoKit conversion by Hao Wan | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Eligible human targets and complete recoverable reply paths only. |
| `deli-corpus` | [DeliData](https://convokit.cornell.edu/documentation/deli.html), Karadzhov, Stafford, and Vlachos | Original distribution's terms apply; no additional license is inferred. MESSAGE original text retained with the quote decoding described above. |
| `diplomacy-corpus` | [Deception in Diplomacy](https://convokit.cornell.edu/documentation/diplomacy.html), Peskov et al. | Original source terms apply; no additional license identifier asserted. Native game splits retained. |
| `fomc-corpus` | [FOMC transcripts](https://convokit.cornell.edu/documentation/fomc.html), Federal Reserve; corpus work by Chenhao Tan and Lillian Lee | Original source terms apply; no additional license identifier asserted. Available speeches ordered by speech index. |
| `gap-corpus` | [Group Affect and Performance](https://convokit.cornell.edu/documentation/gap.html), University of the Fraser Valley; ConvoKit contributors | [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). Complete group discussions retained. |
| `iq2-corpus` | [Intelligence Squared Debates](https://convokit.cornell.edu/documentation/iq2.html), Zhang et al.; ConvoKit conversion by Lucas Van Bramer and Marianne Aubin Le Quere | Original source terms apply; no additional license identifier asserted. Collective audience is context only. |
| `llm-facilitation-corpus` | [ConvoKit release](https://zissou.infosci.cornell.edu/convokit/datasets/llm-facilitation-corpus/); Bringing Everyone to the Table, Microsoft Research/GRAIL | Original release terms apply; the downloaded corpus has no standalone license declaration, so none is inferred. Only source-valid conversations; AI facilitators are context only. |
| `news-interview` | [NewsInterview](https://convokit.cornell.edu/documentation/interview.html), original authors and ConvoKit contributors | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/), as stated in ConvoKit documentation. Human interviews retained. |
| `spolin-corpus` | [SPOLIN](https://convokit.cornell.edu/documentation/spolin.html), Hyundong Justin Cho and Jonathan May | [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). Both yes-and and non-yes-and human-authored pairs retained; native validation preserved. |
Habermas, CANDOR Cliffhanger, Wikipedia Politeness, and Stack Exchange Politeness
are not included. CANDOR Cliffhanger was removed because of transcript and
segmentation quality.
The two politeness corpora were removed because they lack conversational context.
No personas were generated. This collection does not
claim that its conversation/group splits are disjoint by individual participant.
## Topical-Chat
Copyright Amazon.com, Inc. or its affiliates. Original release:
[alexa/Topical-Chat](https://github.com/alexa/Topical-Chat), revision
`7c939229cbcf6f55f6977b341a5a2f2fe982d53f`.
Topical-Chat and these reformatted Topical-Chat files are provided under the
[Community Data License Agreement – Sharing, Version 1.0](https://github.com/alexa/Topical-Chat/blob/7c939229cbcf6f55f6977b341a5a2f2fe982d53f/DATALICENSE).
This source-specific license does not replace the terms of other sources.
Changes: each conversation is exported from both human participants' perspectives;
roles are reassigned so the target human is `assistant`. Frequent and rare native
validation/test partitions are combined into validation/test respectively.
Dialogue wording and order are unchanged. Reading material and ratings are not
included in training messages; no system messages or generated personas are added.
Original split identities and annotations are retained locally.
## CCPE-M
Original release: [Filip Radlinski, Krisztian Balog, Bill Byrne and Karthik Krishnamoorthi, Google LLC](https://github.com/google-research-datasets/ccpe), revision
`2c9cd30f33f3a154b5a27d015333679262ff36f5`.
Terms: [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). These source-specific terms also apply to the reformatted files.
Changes: User-target role reassignment and deterministic conversation-level splits; original dialogue text and order unchanged; annotations omitted.
## CaSiNo
Original release: [Kushal Chawla, Jaysa Ramirez, Rene Clever, Gale Lucas, Jonathan May and Jonathan Gratch](https://github.com/kushalchawla/CaSiNo), revision
`2f6ed4a6a55110152a7699fbaa8150d6036314be`.
Terms: [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). These source-specific terms also apply to the reformatted files.
Changes: Both target-human perspectives; interface actions removed without replacement; own resource priorities and reasons formatted as system context; native splits and remaining dialogue text preserved; other metadata omitted.
## INSPIRED
Original release: [Shirley Anugrah Hayati, Dongyeop Kang, Qingxiaoyang Zhu, Weiyan Shi and Zhou Yu](https://github.com/sweetpeach/Inspired), revision
`782d7c1845d11f75d2542809c7f2a51b63ef0304`.
The source repository does not specify a redistribution license. No license or
independently verified redistribution permission is inferred by this collection.
Changes: Seeker-target role reassignment; literal QUOTATION_MARK decoded to double quotes; other text, utterance boundaries, and native splits unchanged; annotations and surveys omitted.

The released files contain 801 training, 99 development, and 99 test conversations,
despite the source documentation describing 100 development and 100 test conversations.
The available conversations are retained without fabricating missing records.

## MutualFriends

Original release: [Stanford NLP's MutualFriends](https://stanfordnlp.github.io/cocoa/),
with the native human–human training conversations distributed by
[ParlAI's pinned loader](https://github.com/facebookresearch/ParlAI/blob/a29567f7ce76992fd1f03c51ba9e3b155a37ea51/parlai/tasks/mutualfriends/agents.py).
The downloaded data file does not include a dataset-specific redistribution
license declaration. No blanket license or independently verified redistribution
permission is inferred from the software license.

Changes: selections and blank messages removed; conversations with fewer than two
speech messages or only one speaking participant excluded. Two human perspectives
are created, with only each target's own friends table and the task goal in system
context. Speech wording, boundaries and order are unchanged. New approximately
90/5/5 train/validation/test splits keep scenarios together within the originally
training-only release. No generated personas or action paraphrases are added.
Some retained speech refers to removed selection events; this conversion does not
claim every exchange is self-contained without the original interface.

## Deal or No Deal

Original release: [Facebook AI Research, end-to-end-negotiator](https://github.com/facebookresearch/end-to-end-negotiator/tree/bbb93bbf00f69fced75d5c0d22e855bda07c9b78).
The source LICENSE is [CC BY-NC 4.0](https://github.com/facebookresearch/end-to-end-negotiator/blob/bbb93bbf00f69fced75d5c0d22e855bda07c9b78/LICENSE).
These source-specific terms apply to the reformatted files and do not change
licenses of other datasets in this collection.

Changes: preserve the existing participant perspectives once each; target speech
becomes assistant, other speech becomes user. Formatting markers and selection
actions are removed. The target's own item quantities and per-item utilities
are formatted as system context; opponent utilities and final allocations are
not included. Released speech strings and official splits are preserved, with
val named validation. Single-utterance records are excluded; valid unmatched
perspectives and failed negotiations remain. No missing counterpart is generated.

The downloaded release has 12,234 perspective records. Removing 64 single-utterance
records leaves 12,170, including 338 unmatched perspectives. Counterpart groups
are inferred from ordered speech and aligned goals; original dialogue IDs are
not supplied, so exported IDs identify native split files and line numbers.

## Taskmaster-4

Original release: [Taskmaster-4, Bill Byrne and Saravanan Ganesh, Google LLC](https://github.com/google-research-datasets/Taskmaster/tree/d92cb6af3005f1dc09c39e75e7daf4a04905e00b/TM-4-2024).
The source README specifies [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
These source-specific terms apply to the reformatted files and do not change
the terms of other datasets in this collection.

Changes: export one customer perspective per conversation; original customer
speech becomes assistant and coffee-agent speech becomes user. Spoken wording,
boundaries, order, short exchanges, and unanswered customer endings are retained.
Collection templates and API annotations are omitted; no system messages or
personas are added. These are human-authored self-dialogues, with some fixed
agent replies used only as context. The separate reward examples are not included.

The eight downloaded files contain 3,710 conversations, versus the README's
claim of 6,500. Retaining the smallest source ID in each complete-dialogue
duplicate group removes 23 extra copies and leaves 3,687 customer perspectives.
Deterministic conversation-level splits contain 3,319 train, 184 validation,
and 184 test rows. These are newly created holdouts; templates can appear in
multiple splits. Raw source identities and duplicate mappings remain local.

## MSC

Original release: [Multi-Session Chat, Jing Xu, Arthur Szlam and Jason Weston,
Facebook AI Research](https://parl.ai/projects/msc/). Retrieved using the ParlAI
release pinned to `a29567f7ce76992fd1f03c51ba9e3b155a37ea51`.

Changes: two human perspectives for eligible native sessions 2–5. Each target's
original persona and supplied previous sessions are labeled system context;
only current-session speech is a training target. Session 1 is not imported as
additional training rows. Explicit speaker identities, wording, chronology and
native chain holdouts are preserved. Malformed speech and unresolved context
conflicts are withheld. Personas are not generated; summaries and opponent
personas are not included.

## FITS

Original release: [Feedback for Interactive Talk & Search, Jing Xu, Megan Ung,
Mojtaba Komeili, Kushal Arora, Y-Lan Boureau and Jason Weston,
Meta AI](https://parl.ai/projects/fits/). Retrieved using the same pinned ParlAI
release. Only v2 and unseen-task test conversations are considered; the earlier
release is not concatenated.

Changes: human requests and corrections become assistant messages, while bot
responses remain user context. Original task instructions and success requirements
are system context. Two documented bot control markers are removed. Search
documents, outcome labels and separately stored correction fields are omitted.
Reviewed self-contained feedback is retained; missing-context and unresolved
review cases remain outside this publication. This is a conservative subset,
not the entire FITS release. Native splits and unseen-task test assignments remain.

For MSC and FITS, the downloaded data files and inspected project pages do not
provide a separate data-license declaration. The ParlAI software license is not
asserted to license these datasets. Attribution is retained without assigning an
invented or blanket dataset license; applicable source terms remain unchanged.

## Humanual-Book

Source: [Humanual-Book, Shirley Wu and colleagues, HumanLM / snap-stanford](https://huggingface.co/datasets/snap-stanford/humanual-book/tree/8272ed5890ae2f226d563fe5edccd14847da0653),
derived from Amazon Reviews 2023. The source declares
[CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/); these
source-specific terms apply to this subset, without assigning a blanket license
to the collection. Credit: Wu, Choi, Khatua, Wang, He-Yueya, Weerasooriya, Wei,
Yang, Leskovec, and Zou, *HUMANLM: Simulating Users with State Alignment Beats
Response Imitation* (2026).

Each row contains a supplied persona as system context, a book title and ordered
description paragraphs as user context, and the source review title and text as
the assistant target. These are review/preference examples, not multi-turn chats.
Ratings, reviewer metadata, and images are not rendered. Supplied personas are
inferred upstream and preserved unchanged; no new personas are generated.

Changes: unpack book descriptions; convert HTML line/paragraph boundaries to
newlines; decode entities once and remove recognized formatting tags and
stylesheet-only HTML comments. Preserve
wording, spelling, punctuation, and literal angle-bracket text. The earliest
review is retained in each of two confirmed duplicate pairs in training. Native
splits are preserved, renaming val to validation: 34,168 train, 492 validation,
and 1,960 test examples. Original IDs and all cleanup/duplicate decisions are
retained in local audits.

The supplied personas may overlap with target reviews; this has not been ruled
out. Reviewers also occur across native splits. These holdouts must not be
described as verified leakage-free or as unseen-reviewer evaluations. Automated
consistency checks confirm source transformation, not authorship or semantic
quality of every individual review.

Sampled source-quality finding: one test example for product `B09M7MR24D` has
a notebook title but a review about a pillow cover. The source product IDs agree;
the original pairing is retained and flagged locally rather than guessed away.
Literal source typos and expressive angle-bracket labels are also preserved.

## Humanual-News, Opinion, Politics, Chat and Email

Original releases from [HumanLM / snap-stanford](https://humanlm.stanford.edu/), not OdysSim copies. Credit: Wu, Choi, Khatua, Wang, He-Yueya, Weerasooriya, Wei, Yang, Leskovec and Zou, *HUMANLM: Simulating Users with State Alignment Beats Response Imitation* (2026).

| Subset | Pinned source | Train | Validation | Test | Source terms |
| --- | --- | ---: | ---: | ---: | --- |
| Humanual-News | [source](https://huggingface.co/datasets/snap-stanford/humanual-news/tree/18f69182151c3591020ddbba76845a336d35ef12) | 45,587 | 706 | 2,270 | Not specified in the pinned dataset card |
| Humanual-Opinion | [source](https://huggingface.co/datasets/snap-stanford/humanual-opinion/tree/26afa1a5cfbc2d4318be56ffe0851c29e1b38b81) | 37,791 | 1,177 | 3,364 | [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) |
| Humanual-Politics | [source](https://huggingface.co/datasets/snap-stanford/humanual-politics/tree/f4a289dd8d2163f7a9759770344b464664d8c1e0) | 45,425 | 489 | 1,987 | [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) |
| Humanual-Chat | [source](https://huggingface.co/datasets/snap-stanford/humanual-chat/tree/25ba779f7f9e35273b70c8f883b9fa655cc236d4) | 23,085 | 477 | 1,096 | [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) |
| Humanual-Email | [source](https://huggingface.co/datasets/snap-stanford/humanual-email/tree/895c918ef386229106ba8689e78910e71cb9548b) | 6,217 | 129 | 514 | [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) |

Underlying sources are BBC/CNN YouTube posts and comments (News), Reddit discussions
(Opinion), Medium articles and replies (Politics), WildChat human–AI exchanges
(Chat), and Enron email threads (Email). Source-specific and underlying-source
terms apply; no blanket license is assigned to this collection or inferred for News.

Each row preserves one human reply as its sole assistant target. Opinion uses
the original comment in metadata.body, restoring 2,168 source completions changed
by upstream preprocessing (including 1,773 with quote-elision markers). The other
four subsets retain their released completion text. Example identities and
official split membership are unchanged by this restoration. Earlier prompt
history remains as released, including any upstream quote-elision markers in
context; restoration applies to target comments only.
The supplied persona remains system context; prior messages appear as a labeled
user-context block. Previous human speech and AI replies are context only. Text,
code, punctuation, whitespace and message order are preserved without HTML cleanup.
News titles and descriptions and Email subjects come from source metadata.
Empty News descriptions use their supplied titles; missing Email subjects remain
absent. No new personas, missing dialogue, or media context are generated.

Confirmed duplicate replies are kept once, preferring test, then validation,
then train. Blank targets are excluded; conflicting native target versions or
personas are quarantined locally. Identical Email threads attributed to different users are also quarantined.
In other sources, identical wording from distinct identified authors is retained
with an audit. Original identities, split assignments,
speaker mappings, exclusions and duplicate decisions are kept locally.

All retained examples keep their native split; val is named validation. News's
additional full partition is not imported. Chat uses **turn holdouts**, with
earlier and later targets from the same conversation across splits. This is not
an unseen-conversation evaluation. Chat history labels are hashed IPs, which can
change within a released conversation; distinct labels are preserved without
claiming they identify distinct people or the same person. Where anonymized
target IDs cannot be linked to historical authors, neutral labels preserve the
original speaker structure without guessing.

These are supplementary per-reply user-behavior examples, not necessarily complete
chats. Personas are supplied upstream and often inferred. Persona/target overlap,
individual human authorship and semantic coherence of every record have not been
independently established. Links, images, videos and attachments may refer to
material absent from the released text. Native splits are not claimed to be
verified leakage-free. Automated source comparison and sampled review validate
the conversion within these limits.

	# Source attribution and terms

	This release reformats existing human-authored conversations into participant
	perspectives. The changes are role assignment, group-preserving train/validation/test
	splits, explicit source instruction extraction, and the source-specific filtering
	listed below. Target utterance wording is retained, except that DeliData export
	quoting is decoded: matching outer single quotes are removed and doubled apostrophes
	within those wrapped strings become single apostrophes. INSPIRED also decodes its
	literal QUOTATION_MARK token to a double quote. Other retained text is unchanged.
	Human-authored scripted material
	is included; AI messages can appear as context but are not training targets.

	Each source retains its applicable terms. No blanket license is assigned by this
	collection. The source pages below provide the original releases, credits, and
	citation details. License identifiers are given only where explicitly documented.

	\| Dataset value (prefix `convokit/`) \| Source and attribution \| Terms / processing \|
	\| --- \| --- \| --- \|
	\| `contextual-abuse` \| [Contextual Abuse Dataset](https://convokit.cornell.edu/documentation/cad.html), Vidgen et al.; ConvoKit conversion by Hao Wan \| [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Eligible human targets and complete recoverable reply paths only. \|
	\| `deli-corpus` \| [DeliData](https://convokit.cornell.edu/documentation/deli.html), Karadzhov, Stafford, and Vlachos \| Original distribution's terms apply; no additional license is inferred. MESSAGE original text retained with the quote decoding described above. \|
	\| `diplomacy-corpus` \| [Deception in Diplomacy](https://convokit.cornell.edu/documentation/diplomacy.html), Peskov et al. \| Original source terms apply; no additional license identifier asserted. Native game splits retained. \|
	\| `fomc-corpus` \| [FOMC transcripts](https://convokit.cornell.edu/documentation/fomc.html), Federal Reserve; corpus work by Chenhao Tan and Lillian Lee \| Original source terms apply; no additional license identifier asserted. Available speeches ordered by speech index. \|
	\| `gap-corpus` \| [Group Affect and Performance](https://convokit.cornell.edu/documentation/gap.html), University of the Fraser Valley; ConvoKit contributors \| [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). Complete group discussions retained. \|
	\| `iq2-corpus` \| [Intelligence Squared Debates](https://convokit.cornell.edu/documentation/iq2.html), Zhang et al.; ConvoKit conversion by Lucas Van Bramer and Marianne Aubin Le Quere \| Original source terms apply; no additional license identifier asserted. Collective audience is context only. \|
	\| `llm-facilitation-corpus` \| [ConvoKit release](https://zissou.infosci.cornell.edu/convokit/datasets/llm-facilitation-corpus/); Bringing Everyone to the Table, Microsoft Research/GRAIL \| Original release terms apply; the downloaded corpus has no standalone license declaration, so none is inferred. Only source-valid conversations; AI facilitators are context only. \|
	\| `news-interview` \| [NewsInterview](https://convokit.cornell.edu/documentation/interview.html), original authors and ConvoKit contributors \| [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/), as stated in ConvoKit documentation. Human interviews retained. \|
	\| `spolin-corpus` \| [SPOLIN](https://convokit.cornell.edu/documentation/spolin.html), Hyundong Justin Cho and Jonathan May \| [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). Both yes-and and non-yes-and human-authored pairs retained; native validation preserved. \|

	Habermas, CANDOR Cliffhanger, Wikipedia Politeness, and Stack Exchange Politeness
	are not included. CANDOR Cliffhanger was removed because of transcript and
	segmentation quality.
	The two politeness corpora were removed because they lack conversational context.
	No personas were generated. This collection does not
	claim that its conversation/group splits are disjoint by individual participant.

	## Topical-Chat

	Copyright Amazon.com, Inc. or its affiliates. Original release:
	[alexa/Topical-Chat](https://github.com/alexa/Topical-Chat), revision
	`7c939229cbcf6f55f6977b341a5a2f2fe982d53f`.
	Topical-Chat and these reformatted Topical-Chat files are provided under the
	[Community Data License Agreement – Sharing, Version 1.0](https://github.com/alexa/Topical-Chat/blob/7c939229cbcf6f55f6977b341a5a2f2fe982d53f/DATALICENSE).
	This source-specific license does not replace the terms of other sources.

	Changes: each conversation is exported from both human participants' perspectives;
	roles are reassigned so the target human is `assistant`. Frequent and rare native
	validation/test partitions are combined into validation/test respectively.
	Dialogue wording and order are unchanged. Reading material and ratings are not
	included in training messages; no system messages or generated personas are added.
	Original split identities and annotations are retained locally.

	## CCPE-M

	Original release: [Filip Radlinski, Krisztian Balog, Bill Byrne and Karthik Krishnamoorthi, Google LLC](https://github.com/google-research-datasets/ccpe), revision
	`2c9cd30f33f3a154b5a27d015333679262ff36f5`.
	Terms: [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). These source-specific terms also apply to the reformatted files.

	Changes: User-target role reassignment and deterministic conversation-level splits; original dialogue text and order unchanged; annotations omitted.

	## CaSiNo

	Original release: [Kushal Chawla, Jaysa Ramirez, Rene Clever, Gale Lucas, Jonathan May and Jonathan Gratch](https://github.com/kushalchawla/CaSiNo), revision
	`2f6ed4a6a55110152a7699fbaa8150d6036314be`.
	Terms: [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). These source-specific terms also apply to the reformatted files.

	Changes: Both target-human perspectives; interface actions removed without replacement; own resource priorities and reasons formatted as system context; native splits and remaining dialogue text preserved; other metadata omitted.

	## INSPIRED

	Original release: [Shirley Anugrah Hayati, Dongyeop Kang, Qingxiaoyang Zhu, Weiyan Shi and Zhou Yu](https://github.com/sweetpeach/Inspired), revision
	`782d7c1845d11f75d2542809c7f2a51b63ef0304`.
	The source repository does not specify a redistribution license. No license or
	independently verified redistribution permission is inferred by this collection.

	Changes: Seeker-target role reassignment; literal QUOTATION_MARK decoded to double quotes; other text, utterance boundaries, and native splits unchanged; annotations and surveys omitted.

	The released files contain 801 training, 99 development, and 99 test conversations,
	despite the source documentation describing 100 development and 100 test conversations.
	The available conversations are retained without fabricating missing records.

	## MutualFriends

	Original release: [Stanford NLP's MutualFriends](https://stanfordnlp.github.io/cocoa/),
	with the native human–human training conversations distributed by
	[ParlAI's pinned loader](https://github.com/facebookresearch/ParlAI/blob/a29567f7ce76992fd1f03c51ba9e3b155a37ea51/parlai/tasks/mutualfriends/agents.py).
	The downloaded data file does not include a dataset-specific redistribution
	license declaration. No blanket license or independently verified redistribution
	permission is inferred from the software license.

	Changes: selections and blank messages removed; conversations with fewer than two
	speech messages or only one speaking participant excluded. Two human perspectives
	are created, with only each target's own friends table and the task goal in system
	context. Speech wording, boundaries and order are unchanged. New approximately
	90/5/5 train/validation/test splits keep scenarios together within the originally
	training-only release. No generated personas or action paraphrases are added.
	Some retained speech refers to removed selection events; this conversion does not
	claim every exchange is self-contained without the original interface.

	## Deal or No Deal

	Original release: [Facebook AI Research, end-to-end-negotiator](https://github.com/facebookresearch/end-to-end-negotiator/tree/bbb93bbf00f69fced75d5c0d22e855bda07c9b78).
	The source LICENSE is [CC BY-NC 4.0](https://github.com/facebookresearch/end-to-end-negotiator/blob/bbb93bbf00f69fced75d5c0d22e855bda07c9b78/LICENSE).
	These source-specific terms apply to the reformatted files and do not change
	licenses of other datasets in this collection.

	Changes: preserve the existing participant perspectives once each; target speech
	becomes assistant, other speech becomes user. Formatting markers and selection
	actions are removed. The target's own item quantities and per-item utilities
	are formatted as system context; opponent utilities and final allocations are
	not included. Released speech strings and official splits are preserved, with
	val named validation. Single-utterance records are excluded; valid unmatched
	perspectives and failed negotiations remain. No missing counterpart is generated.

	The downloaded release has 12,234 perspective records. Removing 64 single-utterance
	records leaves 12,170, including 338 unmatched perspectives. Counterpart groups
	are inferred from ordered speech and aligned goals; original dialogue IDs are
	not supplied, so exported IDs identify native split files and line numbers.

	## Taskmaster-4

	Original release: [Taskmaster-4, Bill Byrne and Saravanan Ganesh, Google LLC](https://github.com/google-research-datasets/Taskmaster/tree/d92cb6af3005f1dc09c39e75e7daf4a04905e00b/TM-4-2024).
	The source README specifies [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
	These source-specific terms apply to the reformatted files and do not change
	the terms of other datasets in this collection.

	Changes: export one customer perspective per conversation; original customer
	speech becomes assistant and coffee-agent speech becomes user. Spoken wording,
	boundaries, order, short exchanges, and unanswered customer endings are retained.
	Collection templates and API annotations are omitted; no system messages or
	personas are added. These are human-authored self-dialogues, with some fixed
	agent replies used only as context. The separate reward examples are not included.

	The eight downloaded files contain 3,710 conversations, versus the README's
	claim of 6,500. Retaining the smallest source ID in each complete-dialogue
	duplicate group removes 23 extra copies and leaves 3,687 customer perspectives.
	Deterministic conversation-level splits contain 3,319 train, 184 validation,
	and 184 test rows. These are newly created holdouts; templates can appear in
	multiple splits. Raw source identities and duplicate mappings remain local.

	## MSC

	Original release: [Multi-Session Chat, Jing Xu, Arthur Szlam and Jason Weston,
	Facebook AI Research](https://parl.ai/projects/msc/). Retrieved using the ParlAI
	release pinned to `a29567f7ce76992fd1f03c51ba9e3b155a37ea51`.

	Changes: two human perspectives for eligible native sessions 2–5. Each target's
	original persona and supplied previous sessions are labeled system context;
	only current-session speech is a training target. Session 1 is not imported as
	additional training rows. Explicit speaker identities, wording, chronology and
	native chain holdouts are preserved. Malformed speech and unresolved context
	conflicts are withheld. Personas are not generated; summaries and opponent
	personas are not included.

	## FITS

	Original release: [Feedback for Interactive Talk & Search, Jing Xu, Megan Ung,
	Mojtaba Komeili, Kushal Arora, Y-Lan Boureau and Jason Weston,
	Meta AI](https://parl.ai/projects/fits/). Retrieved using the same pinned ParlAI
	release. Only v2 and unseen-task test conversations are considered; the earlier
	release is not concatenated.

	Changes: human requests and corrections become assistant messages, while bot
	responses remain user context. Original task instructions and success requirements
	are system context. Two documented bot control markers are removed. Search
	documents, outcome labels and separately stored correction fields are omitted.
	Reviewed self-contained feedback is retained; missing-context and unresolved
	review cases remain outside this publication. This is a conservative subset,
	not the entire FITS release. Native splits and unseen-task test assignments remain.

	For MSC and FITS, the downloaded data files and inspected project pages do not
	provide a separate data-license declaration. The ParlAI software license is not
	asserted to license these datasets. Attribution is retained without assigning an
	invented or blanket dataset license; applicable source terms remain unchanged.

	## Humanual-Book

	Source: [Humanual-Book, Shirley Wu and colleagues, HumanLM / snap-stanford](https://huggingface.co/datasets/snap-stanford/humanual-book/tree/8272ed5890ae2f226d563fe5edccd14847da0653),
	derived from Amazon Reviews 2023. The source declares
	[CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/); these
	source-specific terms apply to this subset, without assigning a blanket license
	to the collection. Credit: Wu, Choi, Khatua, Wang, He-Yueya, Weerasooriya, Wei,
	Yang, Leskovec, and Zou, *HUMANLM: Simulating Users with State Alignment Beats
	Response Imitation* (2026).

	Each row contains a supplied persona as system context, a book title and ordered
	description paragraphs as user context, and the source review title and text as
	the assistant target. These are review/preference examples, not multi-turn chats.
	Ratings, reviewer metadata, and images are not rendered. Supplied personas are
	inferred upstream and preserved unchanged; no new personas are generated.

	Changes: unpack book descriptions; convert HTML line/paragraph boundaries to
	newlines; decode entities once and remove recognized formatting tags and
	stylesheet-only HTML comments. Preserve
	wording, spelling, punctuation, and literal angle-bracket text. The earliest
	review is retained in each of two confirmed duplicate pairs in training. Native
	splits are preserved, renaming val to validation: 34,168 train, 492 validation,
	and 1,960 test examples. Original IDs and all cleanup/duplicate decisions are
	retained in local audits.

	The supplied personas may overlap with target reviews; this has not been ruled
	out. Reviewers also occur across native splits. These holdouts must not be
	described as verified leakage-free or as unseen-reviewer evaluations. Automated
	consistency checks confirm source transformation, not authorship or semantic
	quality of every individual review.

	Sampled source-quality finding: one test example for product `B09M7MR24D` has
	a notebook title but a review about a pillow cover. The source product IDs agree;
	the original pairing is retained and flagged locally rather than guessed away.
	Literal source typos and expressive angle-bracket labels are also preserved.

	## Humanual-News, Opinion, Politics, Chat and Email

	Original releases from [HumanLM / snap-stanford](https://humanlm.stanford.edu/), not OdysSim copies. Credit: Wu, Choi, Khatua, Wang, He-Yueya, Weerasooriya, Wei, Yang, Leskovec and Zou, HUMANLM: Simulating Users with State Alignment Beats Response Imitation (2026).

	\| Subset \| Pinned source \| Train \| Validation \| Test \| Source terms \|
	\| --- \| --- \| ---: \| ---: \| ---: \| --- \|
	\| Humanual-News \| [source](https://huggingface.co/datasets/snap-stanford/humanual-news/tree/18f69182151c3591020ddbba76845a336d35ef12) \| 45,587 \| 706 \| 2,270 \| Not specified in the pinned dataset card \|
	\| Humanual-Opinion \| [source](https://huggingface.co/datasets/snap-stanford/humanual-opinion/tree/26afa1a5cfbc2d4318be56ffe0851c29e1b38b81) \| 37,791 \| 1,177 \| 3,364 \| [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) \|
	\| Humanual-Politics \| [source](https://huggingface.co/datasets/snap-stanford/humanual-politics/tree/f4a289dd8d2163f7a9759770344b464664d8c1e0) \| 45,425 \| 489 \| 1,987 \| [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) \|
	\| Humanual-Chat \| [source](https://huggingface.co/datasets/snap-stanford/humanual-chat/tree/25ba779f7f9e35273b70c8f883b9fa655cc236d4) \| 23,085 \| 477 \| 1,096 \| [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) \|
	\| Humanual-Email \| [source](https://huggingface.co/datasets/snap-stanford/humanual-email/tree/895c918ef386229106ba8689e78910e71cb9548b) \| 6,217 \| 129 \| 514 \| [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) \|

	Underlying sources are BBC/CNN YouTube posts and comments (News), Reddit discussions
	(Opinion), Medium articles and replies (Politics), WildChat human–AI exchanges
	(Chat), and Enron email threads (Email). Source-specific and underlying-source
	terms apply; no blanket license is assigned to this collection or inferred for News.

	Each row preserves one human reply as its sole assistant target. Opinion uses
	the original comment in metadata.body, restoring 2,168 source completions changed
	by upstream preprocessing (including 1,773 with quote-elision markers). The other
	four subsets retain their released completion text. Example identities and
	official split membership are unchanged by this restoration. Earlier prompt
	history remains as released, including any upstream quote-elision markers in
	context; restoration applies to target comments only.
	The supplied persona remains system context; prior messages appear as a labeled
	user-context block. Previous human speech and AI replies are context only. Text,
	code, punctuation, whitespace and message order are preserved without HTML cleanup.
	News titles and descriptions and Email subjects come from source metadata.
	Empty News descriptions use their supplied titles; missing Email subjects remain
	absent. No new personas, missing dialogue, or media context are generated.

	Confirmed duplicate replies are kept once, preferring test, then validation,
	then train. Blank targets are excluded; conflicting native target versions or
	personas are quarantined locally. Identical Email threads attributed to different users are also quarantined.
	In other sources, identical wording from distinct identified authors is retained
	with an audit. Original identities, split assignments,
	speaker mappings, exclusions and duplicate decisions are kept locally.

	All retained examples keep their native split; val is named validation. News's
	additional full partition is not imported. Chat uses turn holdouts, with
	earlier and later targets from the same conversation across splits. This is not
	an unseen-conversation evaluation. Chat history labels are hashed IPs, which can
	change within a released conversation; distinct labels are preserved without
	claiming they identify distinct people or the same person. Where anonymized
	target IDs cannot be linked to historical authors, neutral labels preserve the
	original speaker structure without guessing.

	These are supplementary per-reply user-behavior examples, not necessarily complete
	chats. Personas are supplied upstream and often inferred. Persona/target overlap,
	individual human authorship and semantic coherence of every record have not been
	independently established. Links, images, videos and attachments may refer to
	material absent from the released text. Native splits are not claimed to be
	verified leakage-free. Automated source comparison and sampled review validate
	the conversion within these limits.