DiaLong: Benchmarking Reminiscence in Long-Context Dialogues
How well can transformer-based Large Language Models (LLMs) retain and recall memories in long contexts of dialogue? LLMs are known to forget long-range factual information in their context, yet the field lacks a comprehensive benchmark to quantitatively assess these shortcomings. We present DiaLong, a long-context dialogue dataset and accompanying memory task that standardizes evaluation in this space. It consists of 100 unique conversations created by humans and then augmented with LLMs, extending beyond lengths found in existing datasets. Our selected memory task is fact recall: classify 40 candidate facts about a dialogue as true or false. This task directly probes the model's ability...
| Authors | Owen Bianchi, Samer Aslan, Ryunosuke Saito |
|---|---|
| Primary category | cs.CL |
| Published | 18 October 2026 |
| Last updated at source | 20 October 2026 |
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