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 have garnered widespread attention due to their remarkable performance across various tasks. However%2C to mitigate the issue of hallucinations%2C LLMs often incorporate retrieval-augmented pipeline to provide them with rich external knowledge and context. Nevertheless%2C challenges stem from inaccurate and coarse-grained context retrieved from the retriever. Supplying irrelevant context to the LLMs can result in poorer responses%2C increased inference latency%2C and higher costs. This paper introduces a method called Instruction-Aware Contextual Compression%2C which filters out less informative content%2C thereby accelerating and enhancing the use of LLMs. The experimental results demonstrate that Instruction-Aware Contextual Compression notably reduces memory consumption and minimizes generation latency while maintaining performance levels comparable to those achieved with the use of the full context. Specifically%2C we achieved a 50%25 reduction in context-related costs%2C resulting in a 5%25 reduction in inference memory usage and a 2.2-fold increase in inference speed%2C with only a minor drop of 0.047 in Rouge-1. These findings suggest that our method strikes an effective balance between efficiency and performance.?quality=80&w=800)
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 -- probabilistic models that can simulate environments by reading texts. Agents equipped with these models provide humans with more extensive and efficient control%2C allowing them to simultaneously alter agent behaviors in multiple tasks via natural verbal communication. In this work%2C we take initial steps in developing robust LWMs that can generalize to compositionally novel language descriptions. We design a challenging world modeling benchmark based on the game of MESSENGER (Hanjie et al.%2C 2021)%2C featuring evaluation settings that require varying degrees of compositional generalization. Our experiments reveal the lack of generalizability of the state-of-the-art Transformer model%2C as it offers marginal improvements in simulation quality over a no-text baseline. We devise a more robust model by fusing the Transformer with the EMMA attention mechanism (Hanjie et al.%2C 2021). Our model substantially outperforms the Transformer and approaches the performance of a model with an oracle semantic parsing and grounding capability. To demonstrate the practicality of this model in improving AI safety and transparency%2C we simulate a scenario in which the model enables an agent to present plans to a human before execution%2C and to revise plans based on their language feedback.?quality=80&w=800)
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