Analyzing and Modeling LLM Response Lengths with Extreme Value Theory: Anchoring Effects and Hybrid Distributions (2025.emnlp-main)
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| Challenge: | Existing approaches treat length as an incidental output property rather than a statistically regular phenomenon worthy of rigorous modeling. |
| Approach: | They propose a statistical framework for modeling and controlling large language model response lengths using extreme value theory and cross-validation on Qwen and DeepSeek architectures. |
| Outcome: | The proposed model improves tail fit and generalizability while maintaining generalizzability. |
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| Challenge: | Existing studies neglect to measure the generalization of their methods to other prompt styles and different sizes of LLMs. |
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| Challenge: | Prior studies have compared the decision-making abilities of large language models with those of humans from a psychological perspective. |
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| Challenge: | Large language models (LLMs) are useful for low-resource scenarios and time-restricted applications. |
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