Papers with KAFT
Large Language Models with Controllable Working Memory (2023.findings-acl)
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Daliang Li, Ankit Singh Rawat, Manzil Zaheer, Xin Wang, Michal Lukasik, Andreas Veit, Felix Yu, Sanjiv Kumar
| Challenge: | Large language models (LLMs) have led to a series of breakthroughs in natural language processing due to the massive amounts of world knowledge they memorize during pretraining. |
| Approach: | They propose a method to inject counterfactual and irrelevant contexts into standard supervised datasets to strengthen both controllability and robustness. |
| Outcome: | The proposed method improves controllability and robustness across model architectures and sizes. |