Stanford and Microsoft researchers have proposed an artificial intelligence (AI) approach that uses declarative statements as corrective feedback for neural models with bugs


The methods currently used to fix systematic problems in NLP models are either fragile or time-consuming and prone to shortcuts. Humans, on the other hand, frequently berate each other using natural language. This has inspired recent research on natural language fixes, which are declarative statements that allow developers to provide corrective feedback at the appropriate level of abstraction by modifying the model or adding missing information to the model.

Instead of relying solely on labeled examples, there is a growing body of research on the use of language to provide instruction, supervision, and even inductive biases to models, such as constructing neural representations from language descriptions (Andreas et al., 2018; Murty et al., 2020; Mu et al., 2020), or language-based zero-shot learning (Brown et al., 2020; Hanjie et al. , 2022; Chen et al., 2021). For corrective purposes, when the user interacts with an existing model to improve it, the language has not yet been used correctly.

The neural language correction model has two heads: a trigger head that determines whether a patch should be applied, and an interpretation head that predicts outcomes based on information in the patch. The model is trained in two steps: first on a set of labeled data, then through task-specific fine-tuning. A set of patch templates are used to create patches and labeled synthetic samples during the second fine-tuning step.

The research group used Google’s T5-broad language model to implement their method and compared the results with baselines for binary sentiment analysis and relationship mining in two scenarios: the model original with only a fine adjustment of the tasks (ORIG) and the model obtained after fine correction. -adjustment (ORIG+PF).

The researchers proposed a patching method that separates the process of evaluating the applicability of a patch (gating) from the task of integrating information (interpretation), and they demonstrated that this method significantly improves two tasks, even with a small number of patches. . They also demonstrate that patches are more resistant to potential shortcuts and effective (1-7 patches are equivalent to or greater than 100 examples of fine-tuning).

Their approach is a first step allowing users to “talk” in one step to correct the models.

Some limitations:

Scaling to huge patch libraries is a limitation. The inference time for the method scales linearly with the size of the patch library.

Scaled to more patch types. Currently, developers have to create patch templates in advance based on knowing what types of patch comments they might want to write in the future.

Analysis of several patches. Finally, the method they created can only use one patch at a time, choosing the most relevant one from our patch library.

Therefore, in this study, declarative statements are proposed as natural language fixes, which allow programmers to manage patterns or add information by setting conditions at the appropriate level of abstraction. Thus, it significantly improves accuracy without incurring high computational costs by using declarative sentences as feedback to correct errors in neural models.

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Rishabh Jain, is an intern consultant at MarktechPost. He is currently pursuing a in Computer Science at IIIT, Hyderabad. He is a machine learning enthusiast and has a keen interest in statistical methods in artificial intelligence and data analysis. He is passionate about developing better algorithms for AI.


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