pwc-archive/papers-with-abstracts huggingface dataset:
Robustness Tests for Automatic Machine Translation Metrics with Adversarial Attacks
Authors: Yichen Huang, Timothy Baldwin
Date: 2023-11-01 00:00:00
Tasks: Machine Translation, Sentence, Translation

We investigate MT evaluation metric performance on adversarially-synthesized texts, to shed light on metric robustness. We experiment with word- and character-level attacks on three popular machine translation metrics: BERTScore, BLEURT, and COMET. Our human experiments validate that automatic metrics tend to overpenalize adversarially-degraded translations. We also identify inconsistencies in BERTScore ratings, where it judges the original sentence and the adversarially-degraded one as similar, while judging the degraded translation as notably worse than the original with respect to the reference. We identify patterns of brittleness that motivate more robust metric development.
Style Locality for Controllable Generation with kNN Language Models
Authors: Gilles Nawezi, Lucie Flek, Charles Welch
Date: 2023-11-01 00:00:00

Recent language models have been improved by the addition of external memory. Nearest neighbor language models retrieve similar contexts to assist in word prediction. The addition of locality levels allows a model to learn how to weight neighbors based on their relative location to the current text in source documents, and have been shown to further improve model performance. Nearest neighbor models have been explored for controllable generation but have not examined the use of locality levels. We present a novel approach for this purpose and evaluate it using automatic and human evaluation on politeness, formality, supportiveness, and toxicity textual data. We find that our model is successfully able to control style and provides a better fluency-style trade-off than previous work.
Discourse Relations Classification and Cross-Framework Discourse Relation Classification Through the Lens of Cognitive Dimensions: An Empirical Investigation
Authors: Yingxue Fu
Date: 2023-11-01 00:00:00
Tasks: Classification, Discourse Segmentation, Relation Classification

Existing discourse formalisms use different taxonomies of discourse relations, which require expert knowledge to understand, posing a challenge for annotation and automatic classification. We show that discourse relations can be effectively captured by some simple cognitively inspired dimensions proposed by Sanders et al.(2018). Our experiments on cross-framework discourse relation classification (PDTB & RST) demonstrate that it is possible to transfer knowledge of discourse relations for one framework to another framework by means of these dimensions, in spite of differences in discourse segmentation of the two frameworks. This manifests the effectiveness of these dimensions in characterizing discourse relations across frameworks. Ablation studies reveal that different dimensions influence different types of discourse relations. The patterns can be explained by the role of dimensions in characterizing and distinguishing different relations. We also report our experimental results on automatic prediction of these dimensions.
Distil-Whisper: Robust Knowledge Distillation via Large-Scale Pseudo Labelling
Authors: Sanchit Gandhi, Patrick von Platen, Alexander M. Rush
Date: 2023-11-01 00:00:00
Tasks: Hallucination, Knowledge Distillation, speech-recognition, Speech Recognition

As the size of pre-trained speech recognition models increases, running these large models in low-latency or resource-constrained environments becomes challenging. In this work, we leverage pseudo-labelling to assemble a large-scale open-source dataset which we use to distill the Whisper model into a smaller variant, called Distil-Whisper. Using a simple word error rate (WER) heuristic, we select only the highest quality pseudo-labels for training. The distilled model is 5.8 times faster with 51% fewer parameters, while performing to within 1% WER on out-of-distribution test data in a zero-shot transfer setting. Distil-Whisper maintains the robustness of the Whisper model to difficult acoustic conditions, while being less prone to hallucination errors on long-form audio. Distil-Whisper is designed to be paired with Whisper for speculative decoding, yielding a 2 times speed-up while mathematically ensuring the same outputs as the original model. To facilitate further research in this domain, we make our training code, inference code and models publicly accessible.
AdaSent: Efficient Domain-Adapted Sentence Embeddings for Few-Shot Classification
Authors: Yongxin Huang, Kexin Wang, Sourav Dutta, Raj Nath Patel, Goran Glavaš, Iryna Gurevych
Date: 2023-11-01 00:00:00
Tasks: Classification, Language Modelling, Sentence, Sentence Classification, Sentence Embedding, Sentence-Embedding, Sentence Embeddings

Recent work has found that few-shot sentence classification based on pre-trained Sentence Encoders (SEs) is efficient, robust, and effective. In this work, we investigate strategies for domain-specialization in the context of few-shot sentence classification with SEs. We first establish that unsupervised Domain-Adaptive Pre-Training (DAPT) of a base Pre-trained Language Model (PLM) (i.e., not an SE) substantially improves the accuracy of few-shot sentence classification by up to 8.4 points. However, applying DAPT on SEs, on the one hand, disrupts the effects of their (general-domain) Sentence Embedding Pre-Training (SEPT). On the other hand, applying general-domain SEPT on top of a domain-adapted base PLM (i.e., after DAPT) is effective but inefficient, since the computationally expensive SEPT needs to be executed on top of a DAPT-ed PLM of each domain. As a solution, we propose AdaSent, which decouples SEPT from DAPT by training a SEPT adapter on the base PLM. The adapter can be inserted into DAPT-ed PLMs from any domain. We demonstrate AdaSent's effectiveness in extensive experiments on 17 different few-shot sentence classification datasets. AdaSent matches or surpasses the performance of full SEPT on DAPT-ed PLM, while substantially reducing the training costs. The code for AdaSent is available.
HARE: Explainable Hate Speech Detection with Step-by-Step Reasoning
Authors: Yongjin Yang, Joonkee Kim, Yujin Kim, Namgyu Ho, James Thorne, Se-Young Yun
Date: 2023-11-01 00:00:00
Tasks: Hate Speech Detection

With the proliferation of social media, accurate detection of hate speech has become critical to ensure safety online. To combat nuanced forms of hate speech, it is important to identify and thoroughly explain hate speech to help users understand its harmful effects. Recent benchmarks have attempted to tackle this issue by training generative models on free-text annotations of implications in hateful text. However, we find significant reasoning gaps in the existing annotations schemes, which may hinder the supervision of detection models. In this paper, we introduce a hate speech detection framework, HARE, which harnesses the reasoning capabilities of large language models (LLMs) to fill these gaps in explanations of hate speech, thus enabling effective supervision of detection models. Experiments on SBIC and Implicit Hate benchmarks show that our method, using model-generated data, consistently outperforms baselines, using existing free-text human annotations. Analysis demonstrates that our method enhances the explanation quality of trained models and improves generalization to unseen datasets. Our code is available at https://github.com/joonkeekim/hare-hate-speech.git.
Probing Explicit and Implicit Gender Bias through LLM Conditional Text Generation
Authors: Xiangjue Dong, Yibo Wang, Philip S. Yu, James Caverlee
Date: 2023-11-01 00:00:00
Tasks: Conditional Text Generation, Fairness, Text Generation

Large Language Models (LLMs) can generate biased and toxic responses. Yet most prior work on LLM gender bias evaluation requires predefined gender-related phrases or gender stereotypes, which are challenging to be comprehensively collected and are limited to explicit bias evaluation. In addition, we believe that instances devoid of gender-related language or explicit stereotypes in inputs can still induce gender bias in LLMs. Thus, in this work, we propose a conditional text generation mechanism without the need for predefined gender phrases and stereotypes. This approach employs three types of inputs generated through three distinct strategies to probe LLMs, aiming to show evidence of explicit and implicit gender biases in LLMs. We also utilize explicit and implicit evaluation metrics to evaluate gender bias in LLMs under different strategies. Our experiments demonstrate that an increased model size does not consistently lead to enhanced fairness and all tested LLMs exhibit explicit and/or implicit gender bias, even when explicit gender stereotypes are absent in the inputs.
Detecting Syllable-Level Pronunciation Stress with A Self-Attention Model
Authors: Wang Weiying, Nakajima Akinori
Date: 2023-11-01 00:00:00

One precondition of effective oral communication is that words should be pronounced clearly, especially for non-native speakers. Word stress is the key to clear and correct English, and misplacement of syllable stress may lead to misunderstandings. Thus, knowing the stress level is important for English speakers and learners. This paper presents a self-attention model to identify the stress level for each syllable of spoken English. Various prosodic and categorical features, including the pitch level, intensity, duration and type of the syllable and its nuclei (the vowel of the syllable), are explored. These features are input to the self-attention model, and syllable-level stresses are predicted. The simplest model yields an accuracy of over 88% and 93% on different datasets, while more advanced models provide higher accuracy. Our study suggests that the self-attention model can be promising in stress-level detection. These models could be applied to various scenarios, such as online meetings and English learning.
Entity Alignment Method of Science and Technology Patent based on Graph Convolution Network and Information Fusion
Authors: Runze Fang, Yawen Li, Yingxia Shao, Zeli Guan, Zhe Xue
Date: 2023-11-01 00:00:00
Tasks: Attribute, Entity Alignment, Graph Neural Network

The entity alignment of science and technology patents aims to link the equivalent entities in the knowledge graph of different science and technology patent data sources. Most entity alignment methods only use graph neural network to obtain the embedding of graph structure or use attribute text description to obtain semantic representation, ignoring the process of multi-information fusion in science and technology patents. In order to make use of the graphic structure and auxiliary information such as the name, description and attribute of the patent entity, this paper proposes an entity alignment method based on the graph convolution network for science and technology patent information fusion. Through the graph convolution network and BERT model, the structure information and entity attribute information of the science and technology patent knowledge graph are embedded and represented to achieve multi-information fusion, thus improving the performance of entity alignment. Experiments on three benchmark data sets show that the proposed method Hit@K The evaluation indicators are better than the existing methods.
Semantic Representation Learning of Scientific Literature based on Adaptive Feature and Graph Neural Network
Authors: Hongrui Gao, Yawen Li, Meiyu Liang, Zeli Guan, Zhe Xue
Date: 2023-11-01 00:00:00
Tasks: Graph Attention, Graph Neural Network, Representation Learning

Because most of the scientific literature data is unmarked, it makes semantic representation learning based on unsupervised graph become crucial. At the same time, in order to enrich the features of scientific literature, a learning method of semantic representation of scientific literature based on adaptive features and graph neural network is proposed. By introducing the adaptive feature method, the features of scientific literature are considered globally and locally. The graph attention mechanism is used to sum the features of scientific literature with citation relationship, and give each scientific literature different feature weights, so as to better express the correlation between the features of different scientific literature. In addition, an unsupervised graph neural network semantic representation learning method is proposed. By comparing the mutual information between the positive and negative local semantic representation of scientific literature and the global graph semantic representation in the potential space, the graph neural network can capture the local and global information, thus improving the learning ability of the semantic representation of scientific literature. The experimental results show that the proposed learning method of semantic representation of scientific literature based on adaptive feature and graph neural network is competitive on the basis of scientific literature classification, and has achieved good results.
IBADR: an Iterative Bias-Aware Dataset Refinement Framework for Debiasing NLU models
Authors: Xiaoyue Wang, Xin Liu, Lijie Wang, Yaoxiang Wang, Jinsong Su, Hua Wu
Date: 2023-11-01 00:00:00
Tasks: Natural Language Understanding

As commonly-used methods for debiasing natural language understanding (NLU) models, dataset refinement approaches heavily rely on manual data analysis, and thus maybe unable to cover all the potential biased features. In this paper, we propose IBADR, an Iterative Bias-Aware Dataset Refinement framework, which debiases NLU models without predefining biased features. We maintain an iteratively expanded sample pool. Specifically, at each iteration, we first train a shallow model to quantify the bias degree of samples in the pool. Then, we pair each sample with a bias indicator representing its bias degree, and use these extended samples to train a sample generator. In this way, this generator can effectively learn the correspondence relationship between bias indicators and samples. Furthermore, we employ the generator to produce pseudo samples with fewer biased features by feeding specific bias indicators. Finally, we incorporate the generated pseudo samples into the pool. Experimental results and in-depth analyses on two NLU tasks show that IBADR not only significantly outperforms existing dataset refinement approaches, achieving SOTA, but also is compatible with model-centric methods.
SoulChat: Improving LLMs' Empathy, Listening, and Comfort Abilities through Fine-tuning with Multi-turn Empathy Conversations
Authors: YiRong Chen, Xiaofen Xing, Jingkai Lin, huimin zheng, Zhenyu Wang, Qi Liu, Xiangmin Xu
Date: 2023-11-01 00:00:00

Large language models (LLMs) have been widely applied in various fields due to their excellent capability for memorizing knowledge and chain of thought (CoT). When these language models are applied in the field of psychological counseling, they often rush to provide universal advice. However, when users seek psychological support, they need to gain empathy, trust, understanding and comfort, rather than just reasonable advice. To this end, we constructed a multi-turn empathetic conversation dataset of more than 2 million samples, in which the input is the multi-turn conversation context, and the target is empathetic responses that cover expressions such as questioning, comfort, recognition, listening, trust, emotional support, etc. Experiments have shown that the empathy ability of LLMs can be significantly enhanced when finetuning by using multi-turn dialogue history and responses that are closer to the expression of a psychological consultant.
Syntactic Inductive Bias in Transformer Language Models: Especially Helpful for Low-Resource Languages?
Authors: Luke Gessler, Nathan Schneider
Date: 2023-11-01 00:00:00
Tasks: Inductive Bias

A line of work on Transformer-based language models such as BERT has attempted to use syntactic inductive bias to enhance the pretraining process, on the theory that building syntactic structure into the training process should reduce the amount of data needed for training. But such methods are often tested for high-resource languages such as English. In this work, we investigate whether these methods can compensate for data sparseness in low-resource languages, hypothesizing that they ought to be more effective for low-resource languages. We experiment with five low-resource languages: Uyghur, Wolof, Maltese, Coptic, and Ancient Greek. We find that these syntactic inductive bias methods produce uneven results in low-resource settings, and provide surprisingly little benefit in most cases.
The Mystery of In-Context Learning: A Comprehensive Survey on Interpretation and Analysis
Authors: Yuxiang Zhou, Jiazheng Li, Yanzheng Xiang, Hanqi Yan, Lin Gui, Yulan He
Date: 2023-11-01 00:00:00
Tasks: In-Context Learning, Survey

Understanding in-context learning (ICL) capability that enables large language models (LLMs) to excel in proficiency through demonstration examples is of utmost importance. This importance stems not only from the better utilization of this capability across various tasks, but also from the proactive identification and mitigation of potential risks, including concerns regarding truthfulness, bias, and toxicity, that may arise alongside the capability. In this paper, we present a thorough survey on the interpretation and analysis of in-context learning. First, we provide a concise introduction to the background and definition of in-context learning. Then, we give an overview of advancements from two perspectives: 1) a theoretical perspective, emphasizing studies on mechanistic interpretability and delving into the mathematical foundations behind ICL; and 2) an empirical perspective, concerning studies that empirically analyze factors associated with ICL. We conclude by highlighting the challenges encountered and suggesting potential avenues for future research. We believe that our work establishes the basis for further exploration into the interpretation of in-context learning. Additionally, we have created a repository containing the resources referenced in our survey.
Instructive Decoding: Instruction-Tuned Large Language Models are Self-Refiner from Noisy Instructions
Authors: Taehyeon Kim, Joonkee Kim, Gihun Lee, Se-Young Yun
Date: 2023-11-01 00:00:00
Tasks: Few-Shot NLI, Instruction Following, Natural Language Understanding, Zero-shot Generalization

While instruction-tuned language models have demonstrated impressive zero-shot generalization, these models often struggle to generate accurate responses when faced with instructions that fall outside their training set. This paper presents Instructive Decoding (ID), a simple yet effective approach that augments the efficacy of instruction-tuned models. Specifically, ID adjusts the logits for next-token prediction in a contrastive manner, utilizing predictions generated from a manipulated version of the original instruction, referred to as a noisy instruction. This noisy instruction aims to elicit responses that could diverge from the intended instruction yet remain plausible. We conduct experiments across a spectrum of such noisy instructions, ranging from those that insert semantic noise via random words to others like 'opposite' that elicit the deviated responses. Our approach achieves considerable performance gains across various instruction-tuned models and tasks without necessitating any additional parameter updates. Notably, utilizing 'opposite' as the noisy instruction in ID, which exhibits the maximum divergence from the original instruction, consistently produces the most significant performance gains across multiple models and tasks.
Is GPT Powerful Enough to Analyze the Emotions of Memes?
Authors: Jingjing Wang, Joshua Luo, Grace Yang, Allen Hong, Feng Luo
Date: 2023-11-01 00:00:00
Tasks: Sentiment Analysis

Large Language Models (LLMs), representing a significant achievement in artificial intelligence (AI) research, have demonstrated their ability in a multitude of tasks. This project aims to explore the capabilities of GPT-3.5, a leading example of LLMs, in processing the sentiment analysis of Internet memes. Memes, which include both verbal and visual aspects, act as a powerful yet complex tool for expressing ideas and sentiments, demanding an understanding of societal norms and cultural contexts. Notably, the detection and moderation of hateful memes pose a significant challenge due to their implicit offensive nature. This project investigates GPT's proficiency in such subjective tasks, revealing its strengths and potential limitations. The tasks include the classification of meme sentiment, determination of humor type, and detection of implicit hate in memes. The performance evaluation, using datasets from SemEval-2020 Task 8 and Facebook hateful memes, offers a comparative understanding of GPT responses against human annotations. Despite GPT's remarkable progress, our findings underscore the challenges faced by these models in handling subjective tasks, which are rooted in their inherent limitations including contextual understanding, interpretation of implicit meanings, and data biases. This research contributes to the broader discourse on the applicability of AI in handling complex, context-dependent tasks, and offers valuable insights for future advancements.
ChipNeMo: Domain-Adapted LLMs for Chip Design
Authors: Mingjie Liu, Teodor-Dumitru Ene, Robert Kirby, Chris Cheng, Nathaniel Pinckney, Rongjian Liang, Jonah Alben, Himyanshu Anand, Sanmitra Banerjee, Ismet Bayraktaroglu, Bonita Bhaskaran, Bryan Catanzaro, Arjun Chaudhuri, Sharon Clay, Bill Dally, Laura Dang, Parikshit Deshpande, Siddhanth Dhodhi, Sameer Halepete, Eric Hill, Jiashang Hu, Sumit Jain, Ankit Jindal, Brucek Khailany, George Kokai, Kishor Kunal, Xiaowei Li, Charley Lind, Hao liu, Stuart Oberman, Sujeet Omar, Ghasem Pasandi, Sreedhar Pratty, Jonathan Raiman, Ambar Sarkar, Zhengjiang Shao, Hanfei Sun, Pratik P Suthar, Varun Tej, Walker Turner, Kaizhe Xu, Haoxing Ren
Date: 2023-10-31 00:00:00
Tasks: Chatbot, Domain Adaptation, Retrieval, Script Generation

ChipNeMo aims to explore the applications of large language models (LLMs) for industrial chip design. Instead of directly deploying off-the-shelf commercial or open-source LLMs, we instead adopt the following domain adaptation techniques: domain-adaptive tokenization, domain-adaptive continued pretraining, model alignment with domain-specific instructions, and domain-adapted retrieval models. We evaluate these methods on three selected LLM applications for chip design: an engineering assistant chatbot, EDA script generation, and bug summarization and analysis. Our evaluations demonstrate that domain-adaptive pretraining of language models, can lead to superior performance in domain related downstream tasks compared to their base LLaMA2 counterparts, without degradations in generic capabilities. In particular, our largest model, ChipNeMo-70B, outperforms the highly capable GPT-4 on two of our use cases, namely engineering assistant chatbot and EDA scripts generation, while exhibiting competitive performance on bug summarization and analysis. These results underscore the potential of domain-specific customization for enhancing the effectiveness of large language models in specialized applications.