Ateret Anaby Tavor

Also published as: Ateret Anaby Tavor


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Unveiling Safety Vulnerabilities of Large Language Models
George Kour | Marcel Zalmanovici | Naama Zwerdling | Esther Goldbraich | Ora Fandina | Ateret Anaby Tavor | Orna Raz | Eitan Farchi
Proceedings of the Third Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)

As large language models become more prevalent, their possible harmful or inappropriate responses are a cause for concern. This paper introduces a unique dataset containing adversarial examples in the form of questions, we call AttaQ, designed to provoke such harmful or inappropriate responses. We assess the efficacy of our dataset by analyzing the vulnerabilities of various models when subjected to it. Additionally, we introduce a novel automatic approach for identifying and naming vulnerable semantic regions — input semantic areas for which the model is likely to produce harmful outputs. This is achieved through the application of specialized clustering techniques that consider both the semantic similarity of the input attacks and the harmfulness of the model’s responses.Automatically identifying vulnerable semantic regions enhances the evaluation of model weaknesses, facilitating targeted improvements to its safety mechanisms and overall reliability.

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Predicting Question-Answering Performance of Large Language Models through Semantic Consistency
Ella Rabinovich | Samuel Ackerman | Orna Raz | Eitan Farchi | Ateret Anaby Tavor
Proceedings of the Third Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)

Semantic consistency of a language model is broadly defined as the model’s ability to produce semantically-equivalent outputs, given semantically-equivalent inputs. We address the task of assessing question-answering (QA) semantic consistency of contemporary large language models (LLMs) by manually creating a benchmark dataset with high-quality paraphrases for factual questions, and release the dataset to the community.We further combine the semantic consistency metric with additional measurements suggested in prior work as correlating with LLM QA accuracy, for building and evaluating a framework for factual QA reference-less performance prediction – predicting the likelihood of a language model to accurately answer a question. Evaluating the framework on five contemporary LLMs, we demonstrate encouraging, significantly outperforming baselines, results.

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Reliable and Interpretable Drift Detection in Streams of Short Texts
Ella Rabinovich | Matan Vetzler | Samuel Ackerman | Ateret Anaby Tavor
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track)

Data drift is the change in model input data that is one of the key factors leading to machine learning models performance degradation over time. Monitoring drift helps detecting these issues and preventing their harmful consequences. Meaningful drift interpretation is a fundamental step towards effective re-training of the model. In this study we propose an end-to-end framework for reliable model-agnostic change-point detection and interpretation in large task-oriented dialog systems, proven effective in multiple customer deployments. We evaluate our approach and demonstrate its benefits with a novel variant of intent classification training dataset, simulating customer requests to a dialog system. We make the data publicly available.

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Text Augmentation Using Dataset Reconstruction for Low-Resource Classification
Adir Rahamim | Guy Uziel | Esther Goldbraich | Ateret Anaby Tavor
Findings of the Association for Computational Linguistics: ACL 2023

In the deployment of real-world text classification models, label scarcity is a common problem and as the number of classes increases, this problem becomes even more complex. An approach to addressing this problem is by applying text augmentation methods. One of the more prominent methods involves using the text-generation capabilities of language models. In this paper, we propose Text AUgmentation by Dataset Reconstruction (TAU-DR), a novel method of data augmentation for text classification. We conduct experiments on several multi-class datasets, showing that our approach improves the current state-of-the-art techniques for data augmentation.


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Gaining Insights into Unrecognized User Utterances in Task-Oriented Dialog Systems
Ella Rabinovich | Matan Vetzler | David Boaz | Vineet Kumar | Gaurav Pandey | Ateret Anaby Tavor
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track

The rapidly growing market demand for automatic dialogue agents capable of goal-oriented behavior has caused many tech-industry leaders to invest considerable efforts into task-oriented dialog systems. The success of these systems is highly dependent on the accuracy of their intent identification – the process of deducing the goal or meaning of the user’s request and mapping it to one of the known intents for further processing. Gaining insights into unrecognized utterances – user requests the systems fails to attribute to a known intent – is therefore a key process in continuous improvement of goal-oriented dialog systems. We present an end-to-end pipeline for processing unrecognized user utterances, deployed in a real-world, commercial task-oriented dialog system, including a specifically-tailored clustering algorithm, a novel approach to cluster representative extraction, and cluster naming. We evaluated the proposed components, demonstrating their benefits in the analysis of unrecognized user requests.

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Measuring the Measuring Tools: An Automatic Evaluation of Semantic Metrics for Text Corpora
George Kour | Samuel Ackerman | Eitan Daniel Farchi | Orna Raz | Boaz Carmeli | Ateret Anaby Tavor
Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)

Similarity metrics for text corpora are becoming critical due to the tremendous growth in the number of generative models. These similarity metrics measure the semantic gap between human and machine-generated text on the corpus level. However, standard methods for evaluating the characteristics of these metrics have yet to be established. We propose a set of automatic measures for evaluating the characteristics of semantic similarity metrics for text corpora. Our measures allow us to sensibly compare and identify the strengths and weaknesses of these metrics. We demonstrate the effectiveness of our evaluation measures in capturing fundamental characteristics by comparing it to a collection of classical and state-of-the-art metrics. Our measures revealed that recent metrics are becoming better in identifying semantic distributional mismatch while classical metrics are more sensitive to perturbations in the surface text levels.


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We’ve had this conversation before: A Novel Approach to Measuring Dialog Similarity
Ofer Lavi | Ella Rabinovich | Segev Shlomov | David Boaz | Inbal Ronen | Ateret Anaby Tavor
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Dialog is a core building block of human natural language interactions. It contains multi-party utterances used to convey information from one party to another in a dynamic and evolving manner. The ability to compare dialogs is beneficial in many real world use cases, such as conversation analytics for contact center calls and virtual agent design. We propose a novel adaptation of the edit distance metric to the scenario of dialog similarity. Our approach takes into account various conversation aspects such as utterance semantics, conversation flow, and the participants. We evaluate this new approach and compare it to existing document similarity measures on two publicly available datasets. The results demonstrate that our method outperforms the other approaches in capturing dialog flow, and is better aligned with the human perception of conversation similarity.


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Balancing via Generation for Multi-Class Text Classification Improvement
Naama Tepper | Esther Goldbraich | Naama Zwerdling | George Kour | Ateret Anaby Tavor | Boaz Carmeli
Findings of the Association for Computational Linguistics: EMNLP 2020

Data balancing is a known technique for improving the performance of classification tasks. In this work we define a novel balancing-viageneration framework termed BalaGen. BalaGen consists of a flexible balancing policy coupled with a text generation mechanism. Combined, these two techniques can be used to augment a dataset for more balanced distribution. We evaluate BalaGen on three publicly available semantic utterance classification (SUC) datasets. One of these is a new COVID-19 Q&A dataset published here for the first time. Our work demonstrates that optimal balancing policies can significantly improve classifier performance, while augmenting just part of the classes and under-sampling others. Furthermore, capitalizing on the advantages of balancing, we show its usefulness in all relevant BalaGen framework components. We validate the superiority of BalaGen on ten semantic utterance datasets taken from real-life goaloriented dialogue systems. Based on our results we encourage using data balancing prior to training for text classification tasks.