Datasets:
Remove category distribution tables from dataset card
Browse files
README.md
CHANGED
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@@ -131,30 +131,6 @@ The `users` configuration contains `user_index`, `user_id`, `persona_info`, `num
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Alignment follows the original benchmark: dialogue record 0 corresponds to `qa_user00.json`, record 1 to `qa_user01.json`, and so on. Both tables include the original UUID to validate this join. Preserve the original trajectory/session order and source timestamps when reproducing the temporal evaluation protocol.
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## Release statistics
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### Query types
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| Original label | Instances |
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|---|---:|
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| Advice | 1,699 |
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| Recommendation | 455 |
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| Conversation | 62 |
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### Logical association labels
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| Original label | Instances |
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|---|---:|
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| Goal/Mission Alignment | 287 |
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| Contextual Fit | 284 |
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| Other | 23 |
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| Resource Constraint | 134 |
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| Motivational Orientation | 588 |
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| State Constraint | 609 |
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| Identity Shift | 291 |
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These counts describe the files in this release. Category strings are preserved as released; no relabeling or reassignment is performed during Hub export. Further definitions and examples are available in the [logical association documentation](https://github.com/ThisIsCosine/IMLogic/blob/main/docs/mined_logical_associations.md).
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## Source data and construction
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IMLogic reuses HaluMem-Medium histories and memory entries. Its construction pipeline tags memories, mines candidate logical associations, and generates QA instances through a Generator–Judger–Refiner process followed by expert screening and independent sample-based human evaluation. See the paper and repository for the construction prompts and evaluation protocols.
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Alignment follows the original benchmark: dialogue record 0 corresponds to `qa_user00.json`, record 1 to `qa_user01.json`, and so on. Both tables include the original UUID to validate this join. Preserve the original trajectory/session order and source timestamps when reproducing the temporal evaluation protocol.
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## Source data and construction
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IMLogic reuses HaluMem-Medium histories and memory entries. Its construction pipeline tags memories, mines candidate logical associations, and generates QA instances through a Generator–Judger–Refiner process followed by expert screening and independent sample-based human evaluation. See the paper and repository for the construction prompts and evaluation protocols.
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