ImplexConv: Implicit Reasoning in Multi-Session Conversations

A large-scale dataset developed to evaluate implicit reasoning in long-term, multi-session conversations. It contains 2,364 total examples split into 814 supportive and 1,550 opposed reasoning cases, each with approximately 100 dialogue sessions and multiple question-answer pairs. The dataset was created by Kaylee0501 and was last updated on 2025-04-29.

Use Cases

• Evaluate model performance on implicit reasoning tasks based on the described supportive and opposed reasoning examples
• Benchmark long-term dependency tracking in dialogue based on the described multi-session conversations
• Train models to infer unstated information based on the described question-answer pairs spanning many sessions

Strengths

• Contains 2,364 total examples for evaluating implicit reasoning
• Divided into 814 supportive and 1,550 opposed reasoning cases for targeted analysis
• Each example includes approximately 100 dialogue sessions, providing long-term context

Limitations

• Column-level documentation is absent; field semantics must be inferred after download
• Row count is unknown, which may limit suitability assessment
• Description metadata is limited; actual data quality requires manual inspection after download

Provenance

Source

huggingface

Freshness

Last updated 2025-04-29 23:24:32