%20through%20the%20angle%20of%20time%20directionality%2C%20addressing%20a%20question%20first%20raised%20in%20(Shannon%2C%201951).%20For%20large%20enough%20models%2C%20we%20empirically%20find%20a%20time%20asymmetry%20in%20their%20ability%20to%20learn%20natural%20language:%20a%20difference%20in%20the%20average%20log-perplexity%20when%20trying%20to%20predict%20the%20next%20token%20versus%20when%20trying%20to%20predict%20the%20previous%20one.%20This%20difference%20is%20at%20the%20same%20time%20subtle%20and%20very%20consistent%20across%20various%20modalities%20(language%2C%20model%20size%2C%20training%20time%2C%20...).%20Theoretically%2C%20this%20is%20surprising:%20from%20an%20information-theoretic%20point%20of%20view%2C%20there%20should%20be%20no%20such%20difference.%20We%20provide%20a%20theoretical%20framework%20to%20explain%20how%20such%20an%20asymmetry%20can%20appear%20from%20sparsity%20and%20computational%20complexity%20considerations%2C%20and%20outline%20a%20number%20of%20perspectives%20opened%20by%20our%20results.)
![Evolution of Neural Networks to Language Models [Updated]](https://www.labellerr.com/blog/content/images/2023/05/large-language-model-logical-reasoning.webp)
%20have%20garnered%20widespread%20attention%20due%20to%20their%20remarkable%20performance%20across%20various%20tasks.%20However%2C%20to%20mitigate%20the%20issue%20of%20hallucinations%2C%20LLMs%20often%20incorporate%20retrieval-augmented%20pipeline%20to%20provide%20them%20with%20rich%20external%20knowledge%20and%20context.%20Nevertheless%2C%20challenges%20stem%20from%20inaccurate%20and%20coarse-grained%20context%20retrieved%20from%20the%20retriever.%20Supplying%20irrelevant%20context%20to%20the%20LLMs%20can%20result%20in%20poorer%20responses%2C%20increased%20inference%20latency%2C%20and%20higher%20costs.%20This%20paper%20introduces%20a%20method%20called%20Instruction-Aware%20Contextual%20Compression%2C%20which%20filters%20out%20less%20informative%20content%2C%20thereby%20accelerating%20and%20enhancing%20the%20use%20of%20LLMs.%20The%20experimental%20results%20demonstrate%20that%20Instruction-Aware%20Contextual%20Compression%20notably%20reduces%20memory%20consumption%20and%20minimizes%20generation%20latency%20while%20maintaining%20performance%20levels%20comparable%20to%20those%20achieved%20with%20the%20use%20of%20the%20full%20context.%20Specifically%2C%20we%20achieved%20a%2050%25%20reduction%20in%20context-related%20costs%2C%20resulting%20in%20a%205%25%20reduction%20in%20inference%20memory%20usage%20and%20a%202.2-fold%20increase%20in%20inference%20speed%2C%20with%20only%20a%20minor%20drop%20of%200.047%20in%20Rouge-1.%20These%20findings%20suggest%20that%20our%20method%20strikes%20an%20effective%20balance%20between%20efficiency%20and%20performance.)
![[QA] Large Language Models are Interpretable Learners - YouTube](https://i.ytimg.com/vi/Q5XKb1o1DgY/maxresdefault.jpg)
![Large Language Models: What Is It & Its Applications [Updated]](https://cdn.labellerr.com/Foundation%20Models/Introduction%20to%20LLM/example%20of%20LLMs.webp)
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