SinkTrack: Attention Sink based Context Anchoring for Large Language Models
Large language models (LLMs) suffer from hallucination and context forgetting. Prior studies suggest that attention drift is a primary cause of these problems, where LLMs' focus shifts towards newly generated tokens and away from the initial input context. To counteract this, we make use of a related, intrinsic characteristic of LLMs: attention sink -- the tendency to consistently allocate high attention to the very first token (i.e., <BOS>) of a sequence. Concretely, we propose an advanced context anchoring method, SinkTrack, which treats <BOS> as an information anchor and injects key contextual features (such as those derived from the input image or instruction) into its representation....
| Authors | Xu Liu, Guikun Chen, Wenguan Wang |
|---|---|
| Primary category | cs.CV |
| Published | 11 April 2026 |
| Last updated at source | 16 May 2026 |
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