Neural Information Processing Systems http://nips.cc/

Neuron interpretation offers valuable insights into how knowledge is structured within a deep neural network model. While a number of neuron interpretation methods have been proposed in the literature, the field lacks a comprehensive comparison among these methods. This gap hampers progress due to the absence of standardized metrics and benchmarks. The commonly used evaluation metric has limitations, and creating ground truth annotations for neurons is impractical. Addressing these challenges, we propose an evaluation framework based on voting theory. Our hypothesis posits that neurons consistently identified by different methods carry more significant information. We rigorously assess our framework across a diverse array of neuron interpretation methods. Notable findings include: i) despite the theoretical differences among the methods, neuron ranking methods share over 60% of their rankings when identifying salient neurons, ii) the neuron interpretation methods are most sensitive to the last layer representations, iii) Probeless neuron ranking emerges as the most consistent method.

Despite substantial advancements, Natural Language Processing (NLP) models often require post-training adjustments to enforce business rules, rectify undesired behavior, and align with user values. These adjustments involve operationalizing concepts--dictating desired model responses to certain inputs. However, it's difficult for a single entity to enumerate and define all possible concepts, indicating a need for a multi-user, collaborative model alignment framework. Moreover, the exhaustive delineation of a concept is challenging, and an improper approach can create shortcuts or interfere with original data or other concepts.To address these challenges, we introduce CoAlign, a framework that enables multi-user interaction with the model, thereby mitigating individual limitations. CoAlign aids users in operationalizing their concepts using Large Language Models, and relying on the principle that NLP models exhibit simpler behaviors in local regions. Our main insight is learning a \emph{local} model for each concept, and a \emph{global} model to integrate the original data with all concepts.We then steer a large language model to generate instances within concept boundaries where local and global disagree.Our experiments show CoAlign is effective at helping multiple users operationalize concepts and avoid interference for a variety of scenarios, tasks, and models.

The representations learned by large-scale NLP models such as BERT have been widely used in various tasks. However, the increasing model size of the pre-trained models also brings efficiency challenges, including inference speed and model size when deploying models on mobile devices. Specifically, most operations in BERT consist of matrix multiplications. These matrices are not low-rank and thus canonical matrix decomposition could not find an efficient approximation. In this paper, we observe that the learned representation of each layer lies in a low-dimensional space.

Advancements in Machine Learning & Neural Networks in recent years have led to widespread implementations of Natural Language Processing across a variety of fields with remarkable success, solving a wide range of complicated problems. However, recent research has shown that machine learning models may be vulnerable in a number of ways, putting both the models and the systems theyre used in at risk. In this paper, we intend to analyze and experiment with the best of existing adversarial attack recipes and create new ones. We concentrated on developing a novel adversarial attack strategy on current state-of-the-art machine learning models by producing ambiguous inputs for the models to confound them and then constructing the path to the future development of the robustness of the models. We will develop adversarial instances with maximum perplexity, utilizing machine learning and deep learning approaches in order to trick the models. In our attack recipe, we will analyze several datasets and focus on creating obfuscous adversary examples to put the models in a state of perplexity, and by including the Bangla Language in the field of adversarial attacks. We strictly uphold utility usage reduction and efficiency throughout our work.

The exponential growth of information presents a significant challenge for researchers and professionals seeking to remain at the forefront of their fields and this paper introduces an innovative framework for automatically generating insightful financial digests using the power of Large Language Models (LLMs), specifically Google's Gemini Pro. By leveraging a combination of data extraction from OpenAlex, strategic prompt engineering, and LLM-driven analysis, we demonstrate the automated example of creating a comprehensive digests that generalize key findings, identify emerging trends. This approach addresses the limitations of traditional analysis methods, enabling the efficient processing of vast amounts of unstructured data and the delivery of actionable insights in an easily digestible format. This paper describes how LLMs work in simple words and how we can use their power to help researchers and scholars save their time and stay informed about current trends. Our study includes step-by-step process, from data acquisition and JSON construction to interaction with Gemini and the automated generation of PDF reports, including a link to the project's GitHub repository for broader accessibility and further development.

"concepts"--dictating desired model responses to certain inputs.

Neural Information Processing Systems http://nips.cc/

In addition, a pre-defined search grid is also necessary. With these input parameters, we firstly distribute the total allowed loss into each individual module. First, it's indeed a trade-off between the efficiency and efficacy as the speedup ratio goes higher at the cost of lower Thus, in the real application, users need to decide what's the best We could have chose another cutoff like 1 % accuracy with lower speedup ratio to report, but this won't help too much when comparing different baseline methods. D.1 LSTM result A 2-layer LSTM model is composed of two large matrices layers and one large softmax layer. Thus, despite the matrix is much smaller and well approximated by DRONE, the overall acceleration on GPU is less.