Papers by Kolby Nottingham
Selective Perception: Learning Concise State Descriptions for Language Model Actors (2024.naacl-short)
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| Challenge: | Recent large language models support longer contexts, but requiring them to process redundant or irrelevant data increases inference time and cost. |
| Approach: | They propose a method that leverages a small finetuned LM to sample the minimal set of input features that maximizes the performance of a downstream LM. |
| Outcome: | The proposed method reduces the length of LM actor input by 87% and 99% while improving task success rates by 158% and 54% on NetHack and robot planning. |