Papers › Benchmarking LLMs via Uncertainty Quantification

Benchmarking LLMs via Uncertainty Quantification

23 Jan 2024arXiv:2401.12794archive 2025-07-28

Fanghua Ye, Mingming Yang, Jianhui Pang, Longyue Wang, Derek F. Wong, Emine Yilmaz, Shuming Shi, Zhaopeng Tu

The proliferation of open-source Large Language Models (LLMs) from various institutions has highlighted the urgent need for comprehensive evaluation methods. However, current evaluation platforms, such as the widely recognized HuggingFace open LLM leaderboard, neglect a crucial aspect -- uncertainty, which is vital for thoroughly assessing LLMs. To bridge this gap, we introduce a new benchmarking approach for LLMs that integrates uncertainty quantification. Our examination involves nine LLMs (LLM series) spanning five representative natural language processing tasks. Our findings reveal that: I) LLMs with higher accuracy may exhibit lower certainty; II) Larger-scale LLMs may display greater uncertainty compared to their smaller counterparts; and III) Instruction-finetuning tends to increase the uncertainty of LLMs. These results underscore the significance of incorporating uncertainty in the evaluation of LLMs.

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cal_acc smartyfh/llm-uncertainty-bench/uncertainty_quantification_via_cp.py official repository ran MIT (permissive) · 1a38d8124050f460 · report
cal_coverage smartyfh/llm-uncertainty-bench/uncertainty_quantification_via_cp.py official repository ran MIT (permissive) · 2d8b9343c19529f9 · report
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get_fewshot_exps smartyfh/llm-uncertainty-bench/generate_logits.py official repository ran · our draft was wrong MIT (permissive) · 50b7e2a98d17a3d4 · report
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get_logits_data smartyfh/llm-uncertainty-bench/uncertainty_quantification_via_cp.py official repository ran MIT (permissive) · d27097c4a183befa · report
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get_raw_data smartyfh/llm-uncertainty-bench/uncertainty_quantification_via_cp.py official repository ran MIT (permissive) · f1da4e31dbe5407b · report
APS_CP smartyfh/llm-uncertainty-bench/uncertainty_quantification_via_cp.py official repository unverified MIT (permissive) · 6fc15eb0d745506b · report
LAC_CP smartyfh/llm-uncertainty-bench/uncertainty_quantification_via_cp.py official repository unverified MIT (permissive) · 8f79d57c0ec1f93b · report
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softmax identical code first harvested elsewhere ran · violated contract fingerprinted licence of this copy not recorded · 7f9b15275ca5759c · report

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BenchmarkingUncertainty Quantification

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