LongSocialBench: Do Long-Context LLMs Understand Online Discussion Threads?
Long-context LLMs can now ingest entire online discussion threads, but understanding their social discourse requires more than reading a long document: models must track parent-reply relations, turning points, scoped subtrees, cross-branch contrasts, and participant trajectories. To test this structure-aware social reasoning, we introduce LongSocialBench, a benchmark of 1,462 verified human-authored multiple-choice items drawn from 94 complete Hacker News, Stack Exchange, and Reddit r/ChangeMyView episodes, with a median length of approximately 73K tokens. Each item pairs a complete serialized discussion and reply structure with a four-option question, requiring models to recover structur...
| Authors | Xinyi Liu, Rinat Khaziev, Dilek Hakkani-Tür et al. |
|---|---|
| Primary category | cs.CL |
| Published | 2 October 2026 |
| Last updated at source | 2 October 2026 |
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