| Ghosted Layers: Unconstrained Activation Alignment for Recovering Layer-Pruned LLMs added by Syntology |
2026-05 (from id) |
chenxinrui-tsinghua/LinearPatch/eval/mmlu_eval.py cd763eaf1ac287e7 |
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MIT (permissive) |
| REAM: Merging Improves Pruning of Experts in LLMs added by Syntology |
2026-04 (from id) |
zai-org/glm-simple-evals/evals/mmlu_pro_eval.py 24ff49e5a1d92207 |
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| SnapMLA: Efficient Long-Context MLA Decoding via Hardware-Aware FP8 Quantized Pipelining added by Syntology |
2026-02 (from id) |
meituan-longcat/SGLang-FluentLLM/benchmark/mmlu/bench_sglang.py cd763eaf1ac287e7 |
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Apache-2.0 (permissive) |
| MRAG: Benchmarking Retrieval-Augmented Generation for Bio-medicine added by Syntology |
2026-01 (from id) |
hendrycks/test/evaluate.py cd763eaf1ac287e7 |
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MIT (permissive) |
| When to Ensemble: Identifying Token-Level Points for Stable and Fast LLM Ensembling WHEN TO ENSEMBLE: IDENTIFYING TOKEN-LEVEL POINTS FOR STABLE AND FAST LLM ENSEMBLING added by Syntology |
2025-10 (from id) |
yoon6503/SAFE/run_safe.py c8947f39a6f3b186 |
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| When to Ensemble: Identifying Token-Level Points for Stable and Fast LLM Ensembling WHEN TO ENSEMBLE: IDENTIFYING TOKEN-LEVEL POINTS FOR STABLE AND FAST LLM ENSEMBLING added by Syntology |
2025-10 (from id) |
yoon6503/SAFE/run_safe_mmlu.py 914abaca965ea62f |
unverified |
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| arXiv:2507.11953 |
2025-07 (from id) |
QwenLM/Qwen/eval/evaluate_ceval.py 15a59658a1a9f53d |
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Apache-2.0 (permissive) |
| When Large Multimodal Models Confront Evolving Knowledge:Challenges and Pathways |
30 May 2025 |
pjlab-sys4nlp/llama-moe/smoe/entrypoint/eval/eval_mmlu_moe_0.py cd763eaf1ac287e7 |
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Apache-2.0 (permissive) |
| Your Pre-trained LLM is Secretly an Unsupervised Confidence Calibrator |
22 May 2025 |
ml-stat-Sustech/Disagreement-Aware-Calibration/common/datasets.py 718d1ba9049ace00 |
unverified |
MIT (permissive) |
| Disentangling Memory and Reasoning Ability in Large Language Models |
20 Nov 2024 |
mingyuj666/disentangling-memory-and-reasoning/load_data/data_agent.py b82b37c35d49aafd |
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| LLMCBench: Benchmarking Large Language Model Compression for Efficient Deployment |
28 Oct 2024 |
AboveParadise/LLMCBench/evaluate_mmlu.py cd763eaf1ac287e7 |
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MIT (permissive) |
| LLMCBench: Benchmarking Large Language Model Compression for Efficient Deployment |
28 Oct 2024 |
AboveParadise/LLMCBench/evaluate_advglue.py 984d3567b5126f65 |
unverified |
MIT (permissive) |
| LLMCBench: Benchmarking Large Language Model Compression for Efficient Deployment |
28 Oct 2024 |
AboveParadise/LLMCBench/evaluate_mnli.py 7538653cf2565390 |
unverified |
MIT (permissive) |
| LLMCBench: Benchmarking Large Language Model Compression for Efficient Deployment |
28 Oct 2024 |
AboveParadise/LLMCBench/evaluate_qnli.py dafa37632f3f91bf |
unverified |
MIT (permissive) |
| Shopping MMLU: A Massive Multi-Task Online Shopping Benchmark for Large Language Models |
28 Oct 2024 |
KL4805/ShoppingMMLU/task_wise_eval/utils.py c78d363cf62ef64c |
unverified |
Apache-2.0 (permissive) |
| Multimodal Large Language Models for Inverse Molecular Design with Retrosynthetic Planning |
5 Oct 2024 |
liugangcode/Llamole/launch.py 3c781f40e814fc9a |
ran
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Apache-2.0 (permissive) |
| MMLU-Pro+: Evaluating Higher-Order Reasoning and Shortcut Learning in LLMs |
3 Sep 2024 |
asgsaeid/mmlu-pro-plus/evaluate_from_api.py df80a0e9f84176ff |
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| AdapMoE: Adaptive Sensitivity-based Expert Gating and Management for Efficient MoE Inference |
19 Aug 2024 |
identical code first harvested elsewhere cd763eaf1ac287e7 |
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licence of this copy not recorded |
| Evaluating Fine-Tuning Efficiency of Human-Inspired Learning Strategies in Medical Question Answering |
15 Aug 2024 |
Oxford-AI-for-Society/human-learning-strategies/training/fine_tuning/shared_utils.py 6ea1d20b1706f31c |
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GPL-3.0 (copyleft) · pointer only |
| A deeper look at depth pruning of LLMs |
23 Jul 2024 |
shoaibahmed/llm_depth_pruning/evals/mmlu.py cd763eaf1ac287e7 |
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| RouteLLM: Learning to Route LLMs with Preference Data |
26 Jun 2024 |
lm-sys/routellm/routellm/evals/mmlu/generate_responses.py cd763eaf1ac287e7 |
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Apache-2.0 (permissive) |
| Pruning via Merging: Compressing LLMs via Manifold Alignment Based Layer Merging |
24 Jun 2024 |
sempraety/pruning-via-merging/pipeline.py e65eff7d9e1ab5f3 |
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| CityGPT: Empowering Urban Spatial Cognition of Large Language Models |
20 Jun 2024 |
tsinghua-fib-lab/citygpt/evaluate/city_eval/run_eval.py f6d6626dd2774d1c |
ran · our draft was wrong
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MIT (permissive) |
| CityBench: Evaluating the Capabilities of Large Language Models for Urban Tasks |
20 Jun 2024 |
tsinghua-fib-lab/citybench/citybench/geoqa/run_eval.py fbde55d407dfbc1f |
ran
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MIT (permissive) |
| Data Contamination Can Cross Language Barriers |
19 Jun 2024 |
ShangDataLab/Deep-Contam/inject/run_sft_mmlu.py 16d87a980525188f |
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| MedExQA: Medical Question Answering Benchmark with Multiple Explanations |
10 Jun 2024 |
knowlab/medexqa/evaluate_MedExQA.py 1a8789ccdb8f2890 |
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| MedExQA: Medical Question Answering Benchmark with Multiple Explanations |
10 Jun 2024 |
knowlab/medexqa/evaluate_pipe_MedExQA.py 2836f4edcd88af80 |
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| DuQuant: Distributing Outliers via Dual Transformation Makes Stronger Quantized LLMs |
3 Jun 2024 |
Hsu1023/DuQuant/mmlu_eval.py cd763eaf1ac287e7 |
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MIT (permissive) |
| MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark |
3 Jun 2024 |
tiger-ai-lab/mmlu-pro/evaluate_from_api.py 1b3ff879d7f6d22b |
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Apache-2.0 (permissive) |
| Multiple-Choice Questions are Efficient and Robust LLM Evaluators |
20 May 2024 |
geralt-targaryen/mc-evaluation/dataset_mc.py 3d5d3f0243075f18 |
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MIT (permissive) |
| Bridging the Gap between Different Vocabularies for LLM Ensemble |
15 Apr 2024 |
xydaytoy/eva/ensemble/eva_multi.py ef417469242d0e7f |
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| Advancing LLM Reasoning Generalists with Preference Trees |
2 Apr 2024 |
openbmb/eurus/eval/mmlu/evaluate_mmlu.py cd763eaf1ac287e7 |
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Apache-2.0 (permissive) |
| Mitigating Catastrophic Forgetting in Large Language Models with Self-Synthesized Rehearsal |
2 Mar 2024 |
DeepLearnXMU/SSR/mmlu_test/evaluate.py cd763eaf1ac287e7 |
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Apache-2.0 (permissive) |
| Learning or Self-aligning? Rethinking Instruction Fine-tuning |
28 Feb 2024 |
renmengjie7/self-aligning/eval/my_benchmark_eval.py 32b8aeb6f6c62b62 |
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| Learning or Self-aligning? Rethinking Instruction Fine-tuning |
28 Feb 2024 |
renmengjie7/self-aligning/eval/my_domain_eval.py 64ff6430c270b1c9 |
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| Sparse is Enough in Fine-tuning Pre-trained Large Language Models |
19 Dec 2023 |
song-wx/SIFT/exp/mmlu/eval_mmlu.py cd763eaf1ac287e7 |
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| SGLang: Efficient Execution of Structured Language Model Programs |
12 Dec 2023 |
tginart/sglang/benchmark/mmlu/bench_sglang.py cd763eaf1ac287e7 |
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Apache-2.0 (permissive) |
| Generative Parameter-Efficient Fine-Tuning |
1 Dec 2023 |
savadikarc/gift/language_modeling/math_code_instruct/eval_mmlu.py dec15cc75848a98e |
ran
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MIT (permissive) |
| R-Tuning: Instructing Large Language Models to Say `I Don't Know' |
16 Nov 2023 |
shizhediao/r-tuning/evaluation/MMLU/evaluate.py 434b759fa0cba061 |
ran
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| Can Large Language Model Comprehend Ancient Chinese? A Preliminary Test on ACLUE |
14 Oct 2023 |
isen-zhang/aclue/src/utils.py c7ad8ee11528e78b |
ran
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MIT (permissive) |
| We are what we repeatedly do: Inducing and deploying habitual schemas in persona-based responses |
10 Oct 2023 |
bkane2/habitual-response-generation/src/generate_response.py 24893f346f97c656 |
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| LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models |
9 Oct 2023 |
FranxYao/chain-of-thought-hub/MMLU/run_mmlu_llama.py cd763eaf1ac287e7 |
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MIT (permissive) |
| FinGPT: Open-Source Financial Large Language Models |
9 Jun 2023 |
ai4finance-foundation/finnlp/finnlp/benchmarks/fiqa.py 1b801af1b3425cfa |
unverified |
MIT (permissive) |
| Chain-of-Thought Hub: A Continuous Effort to Measure Large Language Models' Reasoning Performance |
26 May 2023 |
franxyao/chain-of-thought-hub/MMLU/run_mmlu_llama.py cd763eaf1ac287e7 |
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MIT (permissive) |
| Measuring Massive Multitask Chinese Understanding |
25 Apr 2023 |
Felixgithub2017/MMCU/TestBloomz.py a4f2a52c39dd4a34 |
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| GLM-130B: An Open Bilingual Pre-trained Model |
5 Oct 2022 |
jackaduma/ChatGLM-LoRA-RLHF-PyTorch/cover_alpaca2jsonl.py 1b801af1b3425cfa |
unverified |
MIT (permissive) |
| Measuring Massive Multitask Language Understanding |
7 Sep 2020 |
ollmer/mmlu/evaluate.py cd763eaf1ac287e7 |
ran · our draft was wrong
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MIT (permissive) |
| arXiv:2025.findings-emnlp.484 |
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UbiquitousLearning/DroidCall/gen_complex_instructions.py 2a66957120b8345c |
unverified |
MIT (permissive) |
| arXiv:2025.findings-emnlp.484 |
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UbiquitousLearning/DroidCall/gen_instructions.py 6544f5aba9b4a618 |
unverified |
MIT (permissive) |
| arXiv:2025.emnlp-main.67 |
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isyuhaochen/RRC-DSCD/code/sft/sft.py 27282e44111d98c4 |
unverified |
MIT (permissive) |