Can Large Language Models Keep Up? Benchmarking Online Adaptation to Continual Knowledge Streams
LLMs operating in dynamic real-world contexts often encounter knowledge that evolves continuously or emerges incrementally. To remain accurate and effective, models must adapt to newly arriving information on the fly. We introduce Online Adaptation to Continual Knowledge Streams(OAKS) to evaluate this capability, establishing a benchmark for online adaptation over streaming, continually updating knowledge. Specifically, the benchmark is structured as a sequence of fine-grained context chunks where facts change dynamically across time intervals. OAKS comprises two datasets: OAKS-BABI and OAKS-Novel, where individual facts evolve multiple times across context chunks. These datasets include ...
| Authors | Jiyeon Kim, Hyunji Lee, Dylan Zhou et al. |
|---|---|
| Primary category | cs.CL |
| Published | 8 March 2026 |
| Last updated at source | 8 March 2026 |
Models and datasets referencing this paper
- dinobby/MINTEvalDataset
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