Canada

Though state-of-the-art (SOTA) NLP systems have achieved remarkable performance on a variety of language understanding tasks, they primarily focus on questions that have a correct and a definitive answer. However, in real-world applications, users often ask questions that don&#39;t have a definitive answer such as questions about future events, questions lacking necessary details to find the answer, and questions that are ambiguous. Incorrectly answering such questions certainly hampers a system&#39;s reliability and trustworthiness. Can SOTA models accurately identify such questions and provide a reasonable response?

To investigate the above question, we introduce QnotA, a dataset consisting of five different categories of questions that don&#39;t have definitive answers. Furthermore, for each QnotA instance, we also provide a corresponding &#39;QA&#39; instance i.e. an alternate question that &quot;can be&quot; answered. With this data, we formulate three evaluation tasks that test a system&#39;s ability to &#39;identify&#39;, &#39;distinguish&#39;, and &#39;justify&#39; QnotA questions. Through comprehensive experiments, we show that even SOTA models including GPT-3 and Flan T5 do not fare well on these tasks and lack considerably behind the human performance baseline. We conduct a thorough analysis which further leads to several interesting findings such as, despite not being able to accurately identify a QnotA question, GPT-3 on being prompted to output a justification of why the given QnotA question doesn&#39;t have a definitive answer is able to provide a reasonable justification. Finally, we believe our work and findings will encourage and facilitate development of more robust NLP systems that can also reasonably respond to questions that don&#39;t have a definitive answer.

ACL 2023

Can NLP Models &#39;Identify&#39;, &#39;Distinguish&#39;, and &#39;Justify&#39; Questions that Don&#39;t have a Definitive Answer?

trustworthiness

evaluation

robustness

## Welcome to EMNLP 2024! 
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EMNLP 2024

EMNLP 2024 will take place in Miami, Florida from Nov 12th to Nov 16th, 2024, at the Hyatt Regency Miami Hote and on Underline for remote participants.

Though state-of-the-art (SOTA) NLP systems have achieved remarkable performance on a variety of language understanding tasks, they primarily focus on questions that have a correct and a definitive answer. However, in real-world applications, users often ask questions that don't have a definitive answer such as questions about future events, questions lacking necessary details to find the answer, and questions that are ambiguous. Incorrectly answering such questions certainly hampers a system's reliability and trustworthiness. Can SOTA models accurately identify such questions and provide a reasonable response?

To investigate the above question, we introduce QnotA, a dataset consisting of five different categories of questions that don't have definitive answers. Furthermore, for each QnotA instance, we also provide a corresponding 'QA' instance i.e. an alternate question that "can be" answered. With this data, we formulate three evaluation tasks that test a system's ability to 'identify', 'distinguish', and 'justify' QnotA questions. Through comprehensive experiments, we show that even SOTA models including GPT-3 and Flan T5 do not fare well on these tasks and lack considerably behind the human performance baseline. We conduct a thorough analysis which further leads to several interesting findings such as, despite not being able to accurately identify a QnotA question, GPT-3 on being prompted to output a justification of why the given QnotA question doesn't have a definitive answer is able to provide a reasonable justification. Finally, we believe our work and findings will encourage and facilitate development of more robust NLP systems that can also reasonably respond to questions that don't have a definitive answer.

Can NLP Models 'Identify', 'Distinguish', and 'Justify' Questions that Don't have a Definitive Answer?

workshop paper

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
 The conference will be held in Toronto, Canada, July 9-14, 2023. 
Following the succession of the recent conferences in our field, ACL 2023 will adopt a hybrid format.
While the impact of Covid has considerably diminished in terms of traveling, obtaining visas to Canada
entails a very long process. Moreover, the global economic conditions pose challenges for many individuals to travel to conferences. Recognizing these circumstances, we know many participants may not be
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current registered participants, approxiately 30% have chosen to attend the conference virtually. Whether
you join us in person or virtually, we sincerely hope everyone has a remarkable conference experience. 
This General Chair’s message is where I express my gratitude to the many individuals who have made
enormous contributions to the conference over the past year.

Read [**ACL 2023 General Chair's message**](https://docs.google.com/document/d/1WobYM7norbG4dI48s75HfJoD89qgX5a_F-6U8AteLSA/edit?usp=sharing/) in full.

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The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing.

This work analyzes backdoor watermarks in an autoregressive transformer fine-tuned to perform a generative sequence-to-sequence task, specifically summarization. We propose and demonstrate an attack to identify trigger words or phrases by analyzing open ended generations from autoregressive models that have backdoor watermarks inserted. It is shown in our work that triggers based on random common words are easier to identify than those based on single, rare tokens. The attack proposed is easy to implement and only requires access to the model weights. Code used to create the backdoor watermarked models and analyze their outputs is shared at \url{https://github.com/evan-person/findingBackdoorWatermarks}.

GPTs Don't Keep Secrets: Searching for Backdoor Watermark Triggers in Autoregressive Language Models


This paper addresses the ethical concerns arising from the use of unauthorized public data in deep learning models and proposes a novel solution. Specifically, building on the work of Huang et al. (2021), we extend their bi-level optimization approach to generate unlearnable text using a gradient-based search technique. However, although effective, this approach faces practical limitations, including the requirement of batches of instances and model architecture knowledge that is not readily accessible to ordinary users with limited access to their own data. Furthermore, even with semantic-preserving constraints, unlearnable noise can alter the text's semantics. To address these challenges, we extract simple patterns from unlearnable text produced by bi-level optimization and demonstrate that the data remains unlearnable for unknown models. Additionally, these patterns are not instance- or dataset-specific, allowing users to readily apply them to text classification and question-answering tasks, even if only a small proportion of users implement them on their public content. We also open-source codes to generate unlearnable text and assess unlearnable noise to benefit the public and future studies.

Make Text Unlearnable: Exploiting Effective Patterns to Protect Personal Data

As the deployment of pre-trained language models (PLMs) expands, pressing security concerns have arisen regarding the potential for malicious extraction of training data, posing a threat to data privacy. This study is the first to provide a comprehensive survey of training data extraction from PLMs. Our review covers more than 100 key papers in fields such as natural language processing and security. First, preliminary knowledge is recapped and a taxonomy of various definitions of memorization is presented. The approaches for attack and defense are then systemized. Furthermore, the empirical findings of several quantitative studies are highlighted. Finally, future research directions based on this review are suggested.

Training Data Extraction From Pre-trained Language Models: A Survey

Recent studies have revealed that NLP predictive models are vulnerable to adversarial attacks. Most existing studies focused on designing attacks to evaluate the robustness of NLP models in the English language alone. Literature has seen an increasing need for NLP solutions for other languages. We, therefore, ask one natural question: whether state-of-the-art (SOTA) attack methods generalize to other languages. This paper investigates how to adapt SOTA adversarial attack algorithms in English to the Chinese language. Our experiments show that attack methods previously applied to English NLP can generate high-quality adversarial examples in Chinese when combined with proper text segmentation and linguistic constraints. In addition, we demonstrate that the generated adversarial examples can achieve high fluency and semantic consistency by focusing on the Chinese language's morphology and phonology, which in turn can be used to improve the adversarial robustness of Chinese NLP models.

Expanding Scope: Adapting English Adversarial Attacks to Chinese

Backdoor attacks are an insidious security threat against machine learning models. Adversaries can manipulate the predictions of compromised models by inserting triggers into the training phase. Various backdoor attacks have been devised which can achieve nearly perfect attack success without affecting model predictions for clean inputs. Means of mitigating such vulnerabilities are underdeveloped, especially in natural language processing. To fill this gap, we introduce IMBERT, which uses either gradients or self-attention scores derived from victim models to self-defend against backdoor attacks at inference time. Our empirical studies demonstrate that IMBERT can effectively identify up to 98.5% of inserted triggers. Thus, it significantly reduces the attack success rate while attaining competitive accuracy on the clean dataset across widespread insertion-based attacks compared to two baselines. Finally, we show that our approach is model-agnostic, and can be easily ported to several pre-trained transformer models.

IMBERT: Making BERT Immune to Insertion-based Backdoor Attacks

Many social networking sites (SNS) offer machine translation of posts in an effort to increase understanding, engagement, and connectivity between users across language barriers. However, the translations of these posts are still not 100% accurate and can be a cause of misunderstandings that can harm post-authors' professional or personal relationships. An exacerbating factor is on most SNS, authors cannot view the translation of their own posts, nor make corrections to inaccurate translations. This paper reports findings from a survey (N = 189) and an interview (N = 15) to explore users' concerns regarding this automatic form of machine translation. Our findings show that users are concerned about potential inaccuracies in the meaning of the translations of their posts, and would thus appreciate being able to view and potentially correct such translations. Additionally, we found that when users write posts in their native language, they write them for specific audiences, so they do not always want them translated. This underscores the urgency of providing users with more control over the translation of their posts.

On The Real-world Performance of Machine Translation: Exploring Social Media Post-authors' Perspectives

Manually annotated datasets are crucial for training and evaluating Natural Language Processing models. However, recent work has discovered that even widely-used benchmark datasets contain a substantial number of erroneous annotations. This problem has been addressed with Annotation Error Detection (AED) models, which can flag such errors for human re-annotation. However, even though many of these AED methods assume a final curation step in which a human annotator decides whether the annotation is erroneous, they have been developed as static models without any human-in-the-loop component. In this work, we propose ActiveAED, an AED method that can detect errors more accurately by repeatedly querying a human for error corrections in its prediction loop. We evaluate ActiveAED on eight datasets spanning five different tasks and find that it leads to improvements over the state of the art on seven of them, with gains of up to six percentage points in average precision.

ActiveAED: A Human in the Loop Improves Annotation Error Detection

Natural language processing models tend to learn and encode social biases present in the data. One popular approach for addressing such biases is to eliminate encoded information from the model's representations. However, current methods are restricted to removing only linearly encoded information. In this work, we propose Iterative Gradient-Based Projection (IGBP), a novel method for removing non-linear encoded concepts from neural representations. Our method consists of iteratively training neural classifiers to predict a particular attribute we seek to eliminate, followed by a projection of the representation on a hypersurface, such that the classifiers become oblivious to the target attribute. We evaluate the effectiveness of our method on the task of removing gender and race information as sensitive attributes. Our results demonstrate that IGBP is effective in mitigating bias through intrinsic and extrinsic evaluations, with minimal impact on downstream task accuracy.

Shielded Representations: Protecting Sensitive Attributes Through Iterative Gradient-Based Projection

TL;DR: To keep up with the rapid language model development, we propose enlisting the models themselves to help construct bias auditing benchmarks that remain challenging. We apply our methodology to a bias NLI benchmark and create a new, much larger benchmark that uncovers considerably more bias in fine-tuned NLI models. The accuracy of the NLI fine-tuned models, correlated with bias, decreases from 95.3% for the original benchmark to a strikingly low 58.6% for the proposed benchmark. We introduce bias measures that account for different types of model errors and distinguish among pro-/anti-stereotype bias and model error due to model brittleness.


Keeping Up with the Language Models: Robustness-Bias Interplay in NLI Data and Models

Many NLP classification tasks, such as sexism/racism detection or toxicity detection, are based on human values. Yet, human values can vary under diverse cultural conditions. Therefore, we introduce a framework for value-aligned classification that performs prediction based on explicitly written human values in the command. 
Along with the task, we propose a practical approach that distills value-aligned knowledge from large-scale language models (LLMs) to construct value-aligned classifiers in two steps. First, we generate value-aligned training data from LLMs by prompt-based few-shot learning. Next, we fine-tune smaller classification models with the generated data for the task. Empirical results show that our VA-Models surpass multiple baselines by at least 15.56% on the F1-score, including few-shot learning with OPT-175B and existing text augmentation methods. We suggest that using classifiers with explicit human value input improves both inclusivity & explainability in AI.

Next from ACL 2023

GPTs Don't Keep Secrets: Searching for Backdoor Watermark Triggers in Autoregressive Language Models

Similar lecture

Gotcha! Don't trick me with unanswerable questions! Self-aligning Large Language Models for Proactively Responding to Unknown Questions