Papers by Daniel Mingyi Israel
Enabling Autoregressive Models to Fill In Masked Tokens (2026.findings-eacl)
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| Challenge: | Autoregressive (AR) and masked language modeling (MLM) models are incapable of mucked infilling, which is the ability to predict mangled tokens between past and future context. |
| Approach: | They propose a method that leverages the strengths of autoregressive and masked language modeling to achieve state-of-the-art mucked infilling performance. |
| Outcome: | The proposed approach outperforms existing methods on masked infilling tasks. |
The Pitfalls of KV Cache Compression (2026.acl-long)
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| Challenge: | Recent literature has shown minimal degradation of KV cache in multi-instruction prompts . authors show that certain instructions degrade much more rapidly with compression . |
| Approach: | They propose to change KV cache eviction policies to reduce the impact of KV evict bias . they propose to use a 'simple' evviction policy to reduce ejection bias if the LLM is a multi-instruction model . |
| Outcome: | The proposed methods show that certain instructions degrade much faster with compression, causing them to be ignored by the LLM. |