Enhancing Data Privacy in Large Language Models through Private Association Editing

Large Language Models (LLMs) are powerful tools with extensive applications, but their tendency to memorize private information raises significant concerns as private data leakage can easily happen. In this paper, we introduce Private Association Editing (PAE), a novel defense approach for private data leakage. PAE is designed to effectively remove Personally Identifiable Information (PII) without retraining the model. Our approach consists of a four-step procedure: detecting memorized PII, applying PAE cards to mitigate memorization of private data, verifying resilience to targeted data extraction (TDE) attacks, and ensuring consistency in the post-edit LLMs. The versatility and efficiency of PAE, which allows for batch modifications, significantly enhance data privacy in LLMs. Experimental results demonstrate the effectiveness of PAE in mitigating private data leakage. We believe PAE will serve as a critical tool in the ongoing effort to protect data privacy in LLMs, encouraging the development of safer models for real-world applications.

Linguistic Fingerprint in Transformer Models: How Language Variation Influences Parameter Selection in Irony Detection

This paper explores the correlation between linguistic diversity, sentiment analysis and transformer model architectures. We aim to investigate how different English variations impact transformer-based models for irony detection. To conduct our study, we used the EPIC corpus to extract five diverse English variation-specific datasets and applied the KEN pruning algorithm on five different architectures. Our results reveal several similarities between optimal subnetworks, which provide insights into the linguistic variations that share strong resemblances and those that exhibit greater dissimilarities. We discovered that optimal subnetworks across models share at least 60% of their parameters, emphasizing the significance of parameter values in capturing and interpreting linguistic variations. This study highlights the inherent structural similarities between models trained on different variants of the same language and also the critical role of parameter values in capturing these nuances.

Investigating the Impact of Data Contamination of Large Language Models in Text-to-SQL Translation

Understanding textual description to generate code seems to be an achieved capability of instruction-following Large Language Models (LLMs) in zero-shot scenario. However, there is a severe possibility that this translation ability may be influenced by having seen target textual descriptions and the related code. This effect is known as Data Contamination. In this study, we investigate the impact of Data Contamination on the performance of GPT-3.5 in the Text-to-SQL code-generating tasks. Hence, we introduce a novel method to detect Data Contamination in GPTs and examine GPT-3.5's Text-to-SQL performances using the known Spider Dataset and our new unfamiliar dataset Termite. Furthermore, we analyze GPT-3.5's efficacy on databases with modified information via an adversarial table disconnection (ATD) approach, complicating Text-to-SQL tasks by removing structural pieces of information from the database. Our results indicate a significant performance drop in GPT-3.5 on the unfamiliar Termite dataset, even with ATD modifications, highlighting the effect of Data Contamination on LLMs in Text-to-SQL translation tasks.

Less is KEN: a Universal and Simple Non-Parametric Pruning Algorithm for Large Language Models

Neural network pruning has become increasingly crucial due to the complexity of neural network models and their widespread use in various fields. Existing pruning algorithms often suffer from limitations such as architecture specificity, excessive complexity and reliance on complex calculations, rendering them impractical for real-world applications. In this paper, we propose KEN: a straightforward, universal and unstructured pruning algorithm based on Kernel Density Estimation (KDE). KEN aims to construct optimized transformer models by selectively preserving the most significant parameters while restoring others to their pre-training state. This approach maintains model performance while allowing storage of only the optimized subnetwork, leading to significant memory savings. Extensive evaluations on seven transformer models demonstrate that KEN achieves equal or better performance than the original models with a minimum parameter reduction of 25%. In-depth comparisons against other pruning and PEFT algorithms confirm KEN effectiveness. Furthermore, we introduce KEN_viz, an explainable tool that visualizes the optimized model composition and the subnetwork selected by KEN.

Empowering Multi-step Reasoning across Languages via Tree-of-Thoughts

Chain-of-Thought (CoT) prompting empowers the reasoning abilities of Large Language Models (LLMs), eliciting them to solve complex reasoning tasks step-by-step. However, with the success of CoT methods, the ability to deliver multi-step reasoning remains limited to English due to the imbalance in the distribution of the pre-training data, making the other languages a barrier. In this work, we propose a Cross-lingual multi-step reasoning approach, aiming to align reasoning processes across different languages. In particular, our method, through a Self-consistent Cross-lingual prompting mechanism inspired by the Tree-of-Thoughts approach, delivers multi-step reasoning paths in different languages that, during the steps, lead to the final solution. Our experimental evaluations show that our method significantly outperforms existing prompting methods, reducing the number of interactions and achieving state-of-the-art performance.

HANS, are you clever? Clever Hans Effect Analysis of Neural Systems

Instruction-tuned Large Language Models (It-LLMs) have been exhibiting outstanding abilities to reason around cognitive states, intentions, and reactions of all people involved, letting humans guide and comprehend day-to-day social interactions effectively. In fact, several multiple-choice questions (MCQ) benchmarks have been proposed to construct solid assessments of the models' abilities. However, earlier works are demonstrating the presence of inherent "order bias" in It-LLMs, posing challenges to the appropriate evaluation. In this paper, we investigate It-LLMs' resilience abilities towards a series of probing tests using four MCQ benchmarks. Introducing adversarial examples, we show a significant performance gap, mainly when varying the order of the choices, which reveals a selection bias and brings into discussion reasoning abilities. Following a correlation between first positions and model choices due to positional bias, we hypothesized the presence of structural heuristics in the decision-making process of the It-LLMs, strengthened by including significant examples in few-shot scenarios. Finally, by using the Chain-of-Thought (CoT) technique, we elicit the model to reason and mitigate the bias by obtaining more robust models.

A Trip Towards Fairness: Bias and De-Biasing in Large Language Models

An outbreak in the popularity of transformer-based Language Models (such as GPT (Brown et al., 2020) and PaLM (Chowdhery et al., 2022)) has opened the doors to new Machine Learning applications. In particular, in Natural Language Processing and how pre-training from large text, corpora is essential in achieving remarkable results in downstream tasks. However, these Language Models seem to have inherent biases toward certain demographics reflected in their training data. While research has attempted to mitigate this problem, existing methods either fail to remove bias altogether, degrade performance, or are expensive. This paper examines the bias produced by promising Language Models when varying parameters and pre-training data. Finally, we propose a de-biasing technique that produces robust de-bias models that maintain performance on downstream tasks.

PreCog: Exploring the Relation between Memorization and Performance in Pre-trained Language Models

Pre-trained Language Models such as BERT are impressive machines with the ability to memorize, possibly generalized learning examples. We present here a small, focused contribution to the analysis of the interplay between memorization and performance of BERT in downstream tasks. We propose PreCog, a measure for evaluating memorization from pre-training, and we analyze its correlation with the BERT's performance. Our experiments show that highly memorized examples are better classified, suggesting memorization is an essential key to success for BERT.

Exploring Linguistic Properties of Monolingual BERTs with Typological Classification among Languages

The overwhelming success of transformers is a real conundrum stimulating a compelling question: are these machines replicating some traditional linguistic models or discovering radically new theories? In this paper, we propose a novel standpoint to investigate this important question. Using typological similarities among languages, we aim to layer-wise compare transformers for different languages to observe whether these similarities emerge for particular layers. For this investigation, we propose to use Centered kernel alignment to measure similarity among weight matrices. We discovered that syntactic typological similarity is consistent with the similarity among weights in the middle layers. This finding confirms results obtained by syntactically probing BERT and, thus, gives an important confirmation that BERT is replicating traditional linguistic models.

The Dark Side of the Language: Pre-trained Transformers in the DarkNet

Pre-trained Transformers are challenging human performances in many natural language processing tasks. The gigantic datasets used for pre-training seem to be the key for their success on existing tasks. In this paper, we explore how a range of pre-trained natural language understanding models perform on truly novel and unexplored data, provided by classification tasks over a DarkNet corpus. Surprisingly, results show that syntactic and lexical neural networks largely outperform pre-trained Transformers. This seems to suggest that pre-trained Transformers have serious difficulties in adapting to radically novel texts.