Towards Efficient LLMs: Analyzing Computational Bottlenecks and Optimization Strategies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The study major focuses on the efficiency of the current Large Language Model (LLMs). By researching several papers that focus on it, the limitations of the current efficiency in LLM are significant problems that need to be considered by academia. Then, the study will provide some research on the progress of solving issues and explain each solution clearly. Finally, the study will focus on the further needs for developing each solution. This study is conducted on the USER-LLM, OPTIMA, and Infinite-LLM systems that can solve the efficiency problems in LLM and find some benefits in improving LLM efficiency limitations. Experimental results show that some issues in each system need to be solved in further research. This study can explain the main efficiency problems in current LLMs and provide direction for further research. With more research on the efficiency problem, computational costs and response times will decrease, enabling real-time decision-making improvement.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it