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Who is better at math, Jenny or Jingzhen? Uncovering Stereotypes in Large language Models

Who is better at math, Jenny or Jingzhen? Uncovering Stereot...

引用

2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Siddique, Zara Turner, Liam D. Espinosa-Anke, Luis School of Computer Science and Informatics Cardiff University United Kingdom AMPLYFI United Kingdom

ISBN: (纸本)9798891761643

Large language models (LLMs) have been shown to propagate and amplify harmful stereotypes, particularly those that disproportionately affect marginalised *** understand the effect of these stereotypes more comprehensively, we introduce GlobalBias, a dataset of 876k sentences incorporating 40 distinct gender-by-ethnicity groups alongside descriptors typically used in bias literature, which enables us to study a broad set of stereotypes from around the *** use GlobalBias to directly probe a suite of LMs via perplexity, which we use as a proxy to determine how certain stereotypes are represented in the model's internal *** this, we generate character profiles based on given names and evaluate the prevalence of stereotypes in model *** find that the demographic groups associated with various stereotypes remain consistent across model likelihoods and model ***, larger models consistently display higher levels of stereotypical outputs, even when explicitly instructed not to. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

来源：评论

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Experimental Contexts Can Facilitate Robust Semantic Property Inference in language Models, but Inconsistently

Experimental Contexts Can Facilitate Robust Semantic Propert...

引用

2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Misra, Kanishka Ettinger, Allyson Mahowald, Kyle The University of Texas Austin United States Allen Institute for Artificial Intelligence United States

ISBN: (纸本)9798891761643

Recent zero-shot evaluations have highlighted important limitations in the abilities of language models (LMs) to perform meaning extraction. However, it is now well known that LMs can demonstrate radical improvements in the presence of experimental contexts such as in-context examples and instructions. How well does this translate to previously studied meaning-sensitive tasks? We present a case-study on the extent to which experimental contexts can improve LMs' robustness in performing property inheritance-predicting semantic properties of novel concepts, a task that they have been previously shown to fail on. Upon carefully controlling the nature of the in-context examples and the instructions, our work reveals that they can indeed lead to nontrivial property inheritance behavior in LMs. However, this ability is inconsistent: with a minimal reformulation of the task, some LMs were found to pick up on shallow, non-semantic heuristics from their inputs, suggesting that the computational principles of semantic property inference are yet to be mastered by LMs. © 2024 Association for Computational Linguistics.

关键词： Semantics

来源：评论

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Do LLMs Overcome Shortcut Learning? An Evaluation of Shortcut Challenges in Large language Models

Do LLMs Overcome Shortcut Learning? An Evaluation of Shortcu...

引用

2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Yuan, Yu Zhao, Lili Zhang, Kai Zheng, Guangting Liu, Qi State Key Lab of Cognitive Intelligence University of Science and Technology of China China School of Computer Science and Technology University of Science and Technology of China China

ISBN: (纸本)9798891761643

Large language Models (LLMs) have shown remarkable capabilities in various natural language processing tasks. However, LLMs may rely on dataset biases as shortcuts for prediction, which can significantly impair their robustness and generalization capabilities. This paper presents Shortcut Suite, a comprehensive test suite designed to evaluate the impact of shortcuts on LLMs' performance, incorporating six shortcut types, five evaluation metrics, and four prompting strategies. Our extensive experiments yield several key findings: 1) LLMs demonstrate varying reliance on shortcuts for downstream tasks, significantly impairing their performance. 2) Larger LLMs are more likely to utilize shortcuts under zero-shot and few-shot in-context learning prompts. 3) Chain-of-thought prompting notably reduces shortcut reliance and outperforms other prompting strategies, while few-shot prompts generally underperform compared to zero-shot prompts. 4) LLMs often exhibit overconfidence in their predictions, especially when dealing with datasets that contain shortcuts. 5) LLMs generally have a lower explanation quality in shortcut-laden datasets, with errors falling into three types: distraction, disguised comprehension, and logical fallacy. Our findings offer new insights for evaluating robustness and generalization in LLMs and suggest potential directions for mitigating the reliance on shortcuts. The code is available at https://***/yyhappier/***. © 2024 Association for Computational Linguistics.

关键词： Zero-shot learning

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Addressing Linguistic Bias through a Contrastive Analysis of Academic Writing in the NLP Domain

Addressing Linguistic Bias through a Contrastive Analysis of...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Ridley, Robert Wu, Zhen Zhang, Jianbing Huang, Shujian Dai, Xinyu Nanjing Univ Natl Key Lab Novel Software Technol Nanjing Peoples R China Collaborat Innovat Ctr Novel Software Technol & I Nanjing Peoples R China

ISBN: (纸本)9798891760608

It has been well documented that a reviewer's opinion of the nativeness of expression in an academic paper affects the likelihood of it being accepted for publication. Previous works have also shone a light on the stress and anxiety authors who are non-native English speakers experience when attempting to publish in international venues. We explore how this might be a concern in the field of natural language processing (NLP) through conducting a comprehensive statistical analysis of NLP paper abstracts, identifying how authors of different linguistic backgrounds differ in the lexical, morphological, syntactic and cohesive aspects of their writing. Through our analysis, we identify that there are a number of characteristics that are highly variable across the different corpora examined in this paper. This indicates potential for the presence of linguistic bias. Therefore, we outline a set of recommendations to publishers of academic journals and conferences regarding their guidelines and resources for prospective authors in order to help enhance inclusivity and fairness.

关键词： natural language processing systems

来源：评论

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You Make me Feel like a natural Question: Training QA Systems on Transformed Trivia Questions

You Make me Feel like a Natural Question: Training QA System...

引用

2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Kabir, Tasnim Sung, Yoo Yeon Bandyopadhyay, Saptarashmi Zou, Hao Chandra, Abhranil Boyd-Graber, Jordan Lee University of Maryland United States College of Information University of Maryland United States Columbia University United States University of Waterloo Canada

ISBN: (纸本)9798891761643

Training question answering (QA) and information retrieval systems for web queries require large, expensive datasets that are difficult to annotate and time-consuming to ***, while natural datasets of information-seeking questions are often prone to ambiguity or ill-formed, there are troves of freely available, carefully crafted question datasets for many ***, we automatically generate shorter, information-seeking questions, resembling web queries in the style of the natural Questions (NQ) dataset from longer trivia *** a QA system on these transformed questions is a viable strategy for alternating to more expensive training setups showing the F1 score difference of less than six points and contrasting the final systems. © 2024 Association for Computational Linguistics.

关键词： Question answering

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Right for Right Reasons: Large language Models for Verifiable Commonsense Knowledge Graph Question Answering

Right for Right Reasons: Large Language Models for Verifiabl...

引用

2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Toroghi, Armin Guo, Willis Pour, Mohammad Mahdi Abdollah Sanner, Scott University of Toronto Canada Vector Institute of Artificial Intelligence Toronto Canada

ISBN: (纸本)9798891761643

Knowledge Graph Question Answering (KGQA) methods seek to answer natural language questions using the relational information stored in Knowledge Graphs (KGs). With the recent advancements of Large language Models (LLMs) and their remarkable reasoning abilities, there is a growing trend to leverage them for KGQA. However, existing methodologies have only focused on answering factual questions, e.g., "In which city was Silvio Berlusconi's first wife born?", leaving questions involving commonsense reasoning that real-world users may pose more often, e.g., "Do I need separate visas to see the Venus of Willendorf and attend the Olympics this summer?" unaddressed. In this work, we first observe that existing LLM-based methods for KGQA struggle with hallucination on such questions, especially on queries targeting long-tail entities (e.g., non-mainstream and recent entities), thus hindering their applicability in real-world applications especially since their reasoning processes are not easily verifiable. In response, we propose Right for Right Reasons (R3), a commonsense KGQA methodology that allows for a verifiable reasoning procedure by axiomatically surfacing intrinsic commonsense knowledge of LLMs and grounding every factual reasoning step on KG triples. Through experimental evaluations across three different tasks-question answering, claim verification, and preference matching-our findings showcase R3 as a superior approach, outperforming existing methodologies and notably reducing instances of hallucination and reasoning errors. © 2024 Association for Computational Linguistics.

关键词： Knowledge graph

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Creative Problem Solving in Large language and Vision Models - What Would it Take?

Creative Problem Solving in Large Language and Vision Models...

引用

2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Nair, Lakshmi Gizzi, Evana Sinapov, Jivko Georgia Institute of Technology AtlantaGA United States Tufts University MedfordMA United States

ISBN: (纸本)9798891761681

We advocate for a strong integration of Computational Creativity (CC) with research in large language and vision models (LLVMs) to address a key limitation of these models, i.e., creative problem solving. We present preliminary experiments showing how CC principles can be applied to address this limitation. Our goal is to foster discussions on creative problem solving in LLVMs and CC at prestigious ML venues. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Comparative Study of Explainability methods for Legal Outcome Prediction 6

Comparative Study of Explainability Methods for Legal Outcom...

引用

6th natural Legal language processing Workshop 2024, NLLP 2024, co-located with the 2024 conference on empirical methods in natural language processing

作者： Staliunaite, Ieva Raminta Valvoda, Josef Satoh, Ken University of Cambridge United Kingdom University of Copenhagen Denmark National Institute of Informatics Japan

ISBN: (纸本)9798891761834

This paper investigates explainability in natural Legal language processing (NLLP). We study the task of legal outcome prediction of the European Court of Human Rights cases in a ternary classification setup, where a language model is fine-tuned to predict whether an article has been claimed and violated (positive outcome), claimed but not violated (negative outcome) or not claimed at all (null outcome). Specifically, we experiment with three popular NLP explainability methods. Correlating the attribution scores of input-level methods (Integrated Gradients and Contrastive Explanations) with rationales from court rulings, we show that the correlations are very weak, with absolute values of Spearman and Kendall correlation coefficients ranging between 0.003 and 0.094. Furthermore, we use a concept-level interpretability method (Concept Erasure) with human expert annotations of legal reasoning, to show that obscuring legal concepts from the model representation has an insignificant effect on model performance (at most a decline of 0.26 F1). Therefore, our results indicate that automated legal outcome prediction models are not reliably grounded in legal reasoning. ©2024 Association for Computational Linguistics.

关键词： Prediction models

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Does Large language Model Contain Task-Specific Neurons?

Does Large Language Model Contain Task-Specific Neurons?

引用

2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Song, Ran He, Shizhu Jiang, Shuting Xian, Yantuan Gao, Shengxiang Liu, Kang Yu, Zhengtao Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Artificial Intelligence Kunming China The Key Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Science Beijing China

ISBN: (纸本)9798891761643

Large language models (LLMs) have demonstrated remarkable capabilities in comprehensively handling various types of natural language processing (NLP) tasks. However, there are significant differences in the knowledge and abilities required for different tasks. Therefore, it is important to understand whether the same LLM processes different tasks in the same way. Are there specific neurons in a LLM for different tasks? Inspired by neuroscience, this paper pioneers the exploration of whether distinct neurons are activated when a LLM handles different tasks. Compared with current research exploring the neurons of language and knowledge, task-specific neurons present a greater challenge due to their abstractness, diversity, and complexity. To address these challenges, this paper proposes a method for task-specific neuron localization based on Causal Gradient Variation with Special Tokens (CGVST). CGVST identifies task-specific neurons by concentrating on the most significant tokens during task processing, thereby eliminating redundant tokens and minimizing interference from non-essential neurons. Compared to traditional neuron localization methods, our approach can more effectively identify task-specific neurons. We conduct experiments across eight different public tasks. Experiments involving the inhibition and amplification of identified neurons demonstrate that our method can accurately locate task-specific neurons. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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How Susceptible are Large language Models to Ideological Manipulation?

How Susceptible are Large Language Models to Ideological Man...

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2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Chen, Kai He, Zihao Yan, Jun Shi, Taiwei Lerman, Kristina Department of Computer Science University of Southern California United States Information Sciences Institute University of Southern California United States

ISBN: (纸本)9798891761643

Large language Models (LLMs) possess the potential to exert substantial influence on public perceptions and interactions with information. This raises concerns about the societal impact that could arise if the ideologies within these models can be easily manipulated. In this work, we investigate how effectively LLMs can learn and generalize ideological biases from their instruction-tuning data. Our findings reveal a concerning vulnerability: exposure to only a small amount of ideologically driven samples significantly alters the ideology of LLMs. Notably, LLMs demonstrate a startling ability to absorb ideology from one topic and generalize it to even unrelated ones. The ease with which LLMs' ideologies can be skewed underscores the risks associated with intentionally poisoned training data by malicious actors or inadvertently introduced biases by data annotators. It also emphasizes the imperative for robust safeguards to mitigate the influence of ideological manipulations on LLMs. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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