MLAlgo-Bench: Can Machines Implement Machine Learning Algorithms? (2025.findings-emnlp)
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| Challenge: | Currently, the top-performing models achieve a 48.8% task completion rate on realizing machine learning algorithms . |
| Approach: | They propose a benchmark to test machine learning's ability to generate ML code for humans . they propose an automatic evaluation framework with metrics such as task pass rate and time overhead . |
| Outcome: | The proposed benchmark is unique in its focus on interpreting complex human instructions and producing multi-step, high-complexity code. |
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| Challenge: | Existing approaches to automating ML are time-consuming and difficult to understand for human developers. |
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LLM-Powered Benchmark Factory: Reliable, Generic, and Efficient (2026.acl-long)
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Peiwen Yuan, Shaoxiong Feng, Yiwei Li, Xinglin Wang, Yueqi Zhang, Jiayi Shi, Chuyi Tan, Boyuan Pan, Yao Hu, Kan Li
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| Challenge: | *HumanEval* and *MBPP* are two popular benchmarks for Python code generation. |
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Representation and Generation of Machine Learning Test Functions (2024.eacl-srw)
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| Challenge: | Large Language Models (LLMs) have been adopted for ML code generation but their implications are relatively unexplored. |
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| Challenge: | Existing benchmarks for detecting AI-generated code are limited to binary human–machine classification under in-distribution settings. |
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| Challenge: | a tutorial on task instruction is aimed at researchers and practitioners interested in NLP generalization . labeled examples are unlikely to be available in large numbers or do not exist . |
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SUPER: Evaluating Agents on Setting Up and Executing Tasks from Research Repositories (2024.emnlp-main)
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Ben Bogin, Kejuan Yang, Shashank Gupta, Kyle Richardson, Erin Bransom, Peter Clark, Ashish Sabharwal, Tushar Khot
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| Challenge: | Large language models are evaluated via perplexity or performance on downstream tasks, but these benchmarks are too complex and difficult to inspect. |
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MEGA: Multilingual Evaluation of Generative AI (2023.emnlp-main)
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Kabir Ahuja, Harshita Diddee, Rishav Hada, Millicent Ochieng, Krithika Ramesh, Prachi Jain, Akshay Nambi, Tanuja Ganu, Sameer Segal, Mohamed Ahmed, Kalika Bali, Sunayana Sitaram
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TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation (2021.findings-emnlp)
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| Challenge: | Recent advances in generative language models have enabled machines to generate realistic texts. |
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