{"id":33710,"date":"2024-09-05T16:14:33","date_gmt":"2024-09-05T14:14:33","guid":{"rendered":"https:\/\/kinit.sk\/survey-paper-accepted-to-acm-computing-surveys-addressing-sensitivity-of-language-models-to-effects-of-randomness\/"},"modified":"2025-06-27T11:11:46","modified_gmt":"2025-06-27T09:11:46","slug":"survey-paper-accepted-to-acm-computing-surveys-addressing-sensitivity-of-language-models-to-effects-of-randomness","status":"publish","type":"post","link":"https:\/\/kinit.sk\/sk\/survey-paper-accepted-to-acm-computing-surveys-addressing-sensitivity-of-language-models-to-effects-of-randomness\/","title":{"rendered":"Survey paper accepted to ACM Computing Surveys – Addressing sensitivity of language models to effects of randomness"},"content":{"rendered":"
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Did you ever try to replicate the results of a specific machine learning study, but often found different performance numbers and findings to the ones observed in the official study? Or have you ever tried to determine which model can be considered state-of-the-art for a specific task, but found that many studies report contradictory findings in this regard? Or did you ever try the newest method for which everyone claims it leads to a significantly better performance, expecting it could help you progress on your research problem, but only found that it actually underperforms a simple baseline?<\/p>\n<\/div>\n\n

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A common culprit that significantly contributes to all of these problems is uncontrolled randomness in the training and evaluation process. Especially the approaches for dealing with limited labelled data (but to a certain extent also neural networks in general), such as in-context learning, fine-tuning, parameter-efficient fine-tuning or meta-learning, were identified to be sensitive to the effects of uncontrolled randomness. Take for example, in-context learning, where such a simple thing as changing the set of in-context examples, or the order in which the in-context samples are presented to the model can determine whether we get state-of-the-art predictions or random guessing. Similarly, repeating the fine-tuning process multiple times can lead to large deviation in performance, where in some cases the smaller variants can outperform their larger counterparts. <\/p>\n<\/div>\n\n

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This uncontrolled randomness, if not properly addressed, was identified to lead to negative consequences. In comparisons and benchmarks, changing only the random seed or using a different prompt format may lead to completely different model rankings. It may also prohibit an objective comparison between different models, creating an imaginary perception of research progress (due to unintentional cherry-picking), or making the research unreproducible. However, even though the effects of randomness can have significant impact, the focus on addressing them is limited in its extent, mainly when dealing with a limited number of labels.<\/p>\n<\/div>\n\n

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In our newest paper entitled A Survey on Stability <\/em><\/a>of<\/a><\/em> Learning with Limited Labelled Data and its Sensitivity to the Effects of Randomness<\/em><\/a>, which was accepted to the prestigious ACM Computing Surveys<\/a> journal, we provide a comprehensive survey of 415 papers that address the effects of randomness. First, we provide an overview of all the possible sources of randomness in the training (e.g., randomness factors), such as initialisation, data choice or data order, that may lead to lower stability of the learned models. Second, we focus on all tasks for addressing the effects of randomness \u2013 investigation of the impact of different factors is determined across different approaches for learning strategies; determining the underlying origin of the randomness, such the problem of underspecification; and finally the mitigation of the effects, where the impact is reduced, increasing stability without reducing the overall performance of the models.<\/p>\n<\/div>\n\n

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Overall we find that the majority focus is on in-context learning with large language models, especially on choosing a set of high-quality in-context examples. However, there are other parts that are getting more and more attention, including:<\/p>\n<\/div>\n\n

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