pwc-archive/papers-with-abstracts huggingface dataset:
SecQA: A Concise Question-Answering Dataset for Evaluating Large Language Models in Computer Security
Authors: Zefang Liu
Date: 2023-12-26 00:00:00
Tasks: Computer Security, Multiple-choice, Question Answering

In this paper, we introduce SecQA, a novel dataset tailored for evaluating the performance of Large Language Models (LLMs) in the domain of computer security. Utilizing multiple-choice questions generated by GPT-4 based on the "Computer Systems Security: Planning for Success" textbook, SecQA aims to assess LLMs' understanding and application of security principles. We detail the structure and intent of SecQA, which includes two versions of increasing complexity, to provide a concise evaluation across various difficulty levels. Additionally, we present an extensive evaluation of prominent LLMs, including GPT-3.5-Turbo, GPT-4, Llama-2, Vicuna, Mistral, and Zephyr models, using both 0-shot and 5-shot learning settings. Our results, encapsulated in the SecQA v1 and v2 datasets, highlight the varying capabilities and limitations of these models in the computer security context. This study not only offers insights into the current state of LLMs in understanding security-related content but also establishes SecQA as a benchmark for future advancements in this critical research area.
TEILP: Time Prediction over Knowledge Graphs via Logical Reasoning
Authors: Siheng Xiong, Yuan Yang, Ali Payani, James C Kerce, Faramarz Fekri
Date: 2023-12-25 00:00:00
Tasks: Knowledge Graphs, Logical Reasoning, Prediction

Conventional embedding-based models approach event time prediction in temporal knowledge graphs (TKGs) as a ranking problem. However, they often fall short in capturing essential temporal relationships such as order and distance. In this paper, we propose TEILP, a logical reasoning framework that naturally integrates such temporal elements into knowledge graph predictions. We first convert TKGs into a temporal event knowledge graph (TEKG) which has a more explicit representation of time in term of nodes of the graph. The TEKG equips us to develop a differentiable random walk approach to time prediction. Finally, we introduce conditional probability density functions, associated with the logical rules involving the query interval, using which we arrive at the time prediction. We compare TEILP with state-of-the-art methods on five benchmark datasets. We show that our model achieves a significant improvement over baselines while providing interpretable explanations. In particular, we consider several scenarios where training samples are limited, event types are imbalanced, and forecasting the time of future events based on only past events is desired. In all these cases, TEILP outperforms state-of-the-art methods in terms of robustness.
Compositional Generalization in Spoken Language Understanding
Authors: Avik Ray, Yilin Shen, Hongxia Jin
Date: 2023-12-25 00:00:00
Tasks: Spoken Language Understanding

State-of-the-art spoken language understanding (SLU) models have shown tremendous success in benchmark SLU datasets, yet they still fail in many practical scenario due to the lack of model compositionality when trained on limited training data. In this paper, we study two types of compositionality: (a) novel slot combination, and (b) length generalization. We first conduct in-depth analysis, and find that state-of-the-art SLU models often learn spurious slot correlations during training, which leads to poor performance in both compositional cases. To mitigate these limitations, we create the first compositional splits of benchmark SLU datasets and we propose the first compositional SLU model, including compositional loss and paired training that tackle each compositional case respectively. On both benchmark and compositional splits in ATIS and SNIPS, we show that our compositional SLU model significantly outperforms (up to $5\%$ F1 score) state-of-the-art BERT SLU model.
Design and Implementation of a Tool for Extracting Uzbek Syllables
Authors: Ulugbek Salaev, Elmurod Kuriyozov, Gayrat Matlatipov
Date: 2023-12-25 00:00:00

The accurate syllabification of words plays a vital role in various Natural Language Processing applications. Syllabification is a versatile linguistic tool with applications in linguistic research, language technology, education, and various fields where understanding and processing language is essential. In this paper, we present a comprehensive approach to syllabification for the Uzbek language, including rule-based techniques and machine learning algorithms. Our rule-based approach utilizes advanced methods for dividing words into syllables, generating hyphenations for line breaks and count of syllables. Additionally, we collected a dataset for evaluating and training using machine learning algorithms comprising word-syllable mappings, hyphenations, and syllable counts to predict syllable counts as well as for the evaluation of the proposed model. Our results demonstrate the effectiveness and efficiency of both approaches in achieving accurate syllabification. The results of our experiments show that both approaches achieved a high level of accuracy, exceeding 99%. This study provides valuable insights and recommendations for future research on syllabification and related areas in not only the Uzbek language itself, but also in other closely-related Turkic languages with low-resource factor.
Solving Label Variation in Scientific Information Extraction via Multi-Task Learning
Authors: Dong Pham, Xanh Ho, Quang-Thuy Ha, Akiko Aizawa
Date: 2023-12-25 00:00:00
Tasks: Multi-Task Learning

Scientific Information Extraction (ScientificIE) is a critical task that involves the identification of scientific entities and their relationships. The complexity of this task is compounded by the necessity for domain-specific knowledge and the limited availability of annotated data. Two of the most popular datasets for ScientificIE are SemEval-2018 Task-7 and SciERC. They have overlapping samples and differ in their annotation schemes, which leads to conflicts. In this study, we first introduced a novel approach based on multi-task learning to address label variations. We then proposed a soft labeling technique that converts inconsistent labels into probabilistic distributions. The experimental results demonstrated that the proposed method can enhance the model robustness to label noise and improve the end-to-end performance in both ScientificIE tasks. The analysis revealed that label variations can be particularly effective in handling ambiguous instances. Furthermore, the richness of the information captured by label variations can potentially reduce data size requirements. The findings highlight the importance of releasing variation labels and promote future research on other tasks in other domains. Overall, this study demonstrates the effectiveness of multi-task learning and the potential of label variations to enhance the performance of ScientificIE.
EcomGPT-CT: Continual Pre-training of E-commerce Large Language Models with Semi-structured Data
Authors: Shirong Ma, Shen Huang, Shulin Huang, Xiaobin Wang, Yangning Li, Hai-Tao Zheng, Pengjun Xie, Fei Huang, Yong Jiang
Date: 2023-12-25 00:00:00
Tasks: In-Context Learning

Large Language Models (LLMs) pre-trained on massive corpora have exhibited remarkable performance on various NLP tasks. However, applying these models to specific domains still poses significant challenges, such as lack of domain knowledge, limited capacity to leverage domain knowledge and inadequate adaptation to domain-specific data formats. Considering the exorbitant cost of training LLMs from scratch and the scarcity of annotated data within particular domains, in this work, we focus on domain-specific continual pre-training of LLMs using E-commerce domain as an exemplar. Specifically, we explore the impact of continual pre-training on LLMs employing unlabeled general and E-commercial corpora. Furthermore, we design a mixing strategy among different data sources to better leverage E-commercial semi-structured data. We construct multiple tasks to assess LLMs' few-shot In-context Learning ability and their zero-shot performance after instruction tuning in E-commerce domain. Experimental results demonstrate the effectiveness of continual pre-training of E-commerce LLMs and the efficacy of our devised data mixing strategy.
Conditional Variational Autoencoder for Sign Language Translation with Cross-Modal Alignment
Authors: Rui Zhao, Liang Zhang, Biao Fu, Cong Hu, Jinsong Su, Yidong Chen
Date: 2023-12-25 00:00:00
Tasks: cross-modal alignment, Decoder, Sign Language Translation, Translation

Sign language translation (SLT) aims to convert continuous sign language videos into textual sentences. As a typical multi-modal task, there exists an inherent modality gap between sign language videos and spoken language text, which makes the cross-modal alignment between visual and textual modalities crucial. However, previous studies tend to rely on an intermediate sign gloss representation to help alleviate the cross-modal problem thereby neglecting the alignment across modalities that may lead to compromised results. To address this issue, we propose a novel framework based on Conditional Variational autoencoder for SLT (CV-SLT) that facilitates direct and sufficient cross-modal alignment between sign language videos and spoken language text. Specifically, our CV-SLT consists of two paths with two Kullback-Leibler (KL) divergences to regularize the outputs of the encoder and decoder, respectively. In the prior path, the model solely relies on visual information to predict the target text; whereas in the posterior path, it simultaneously encodes visual information and textual knowledge to reconstruct the target text. The first KL divergence optimizes the conditional variational autoencoder and regularizes the encoder outputs, while the second KL divergence performs a self-distillation from the posterior path to the prior path, ensuring the consistency of decoder outputs. We further enhance the integration of textual information to the posterior path by employing a shared Attention Residual Gaussian Distribution (ARGD), which considers the textual information in the posterior path as a residual component relative to the prior path. Extensive experiments conducted on public datasets (PHOENIX14T and CSL-daily) demonstrate the effectiveness of our framework, achieving new state-of-the-art results while significantly alleviating the cross-modal representation discrepancy.
A Split-and-Privatize Framework for Large Language Model Fine-Tuning
Authors: Xicong Shen, Yang Liu, Huiqi Liu, Jue Hong, Bing Duan, Zirui Huang, Yunlong Mao, Ye Wu, Di wu
Date: 2023-12-25 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, parameter-efficient fine-tuning

Fine-tuning is a prominent technique to adapt a pre-trained language model to downstream scenarios. In parameter-efficient fine-tuning, only a small subset of modules are trained over the downstream datasets, while leaving the rest of the pre-trained model frozen to save computation resources. In recent years, a popular productization form arises as Model-as-a-Service (MaaS), in which vendors provide abundant pre-trained language models, server resources and core functions, and customers can fine-tune, deploy and invoke their customized model by accessing the one-stop MaaS with their own private dataset. In this paper, we identify the model and data privacy leakage risks in MaaS fine-tuning, and propose a Split-and-Privatize (SAP) framework, which manage to mitigate the privacy issues by adapting the existing split learning architecture. The proposed SAP framework is sufficiently investigated by experiments, and the results indicate that it can enhance the empirical privacy by 62% at the cost of 1% model performance degradation on the Stanford Sentiment Treebank dataset.
Reducing LLM Hallucinations using Epistemic Neural Networks
Authors: Shreyas Verma, Kien Tran, Yusuf Ali, Guangyu Min
Date: 2023-12-25 00:00:00
Tasks: TruthfulQA

Reducing and detecting hallucinations in large language models is an open research problem. In this project, we attempt to leverage recent advances in the field of uncertainty estimation to reduce hallucinations in frozen large language models. Epistemic neural networks have recently been proposed to improve output joint distributions for large pre-trained models. ENNs are small networks attached to large, frozen models to improve the model's joint distributions and uncertainty estimates. In this work, we train an epistemic neural network on top of the Llama-2 7B model combined with a contrastive decoding feature enhancement technique. We are the first to train an ENN for the next token prediction task and explore the efficacy of this method in reducing hallucinations on the TruthfulQA dataset. In essence, we provide a method that leverages a pre-trained model's latent embeddings to reduce hallucinations.
The Persuasive Power of Large Language Models
Authors: Simon Martin Breum, Daniel Vædele Egdal, Victor Gram Mortensen, Anders Giovanni Møller, Luca Maria Aiello
Date: 2023-12-24 00:00:00
Tasks: Persuasiveness

The increasing capability of Large Language Models to act as human-like social agents raises two important questions in the area of opinion dynamics. First, whether these agents can generate effective arguments that could be injected into the online discourse to steer the public opinion. Second, whether artificial agents can interact with each other to reproduce dynamics of persuasion typical of human social systems, opening up opportunities for studying synthetic social systems as faithful proxies for opinion dynamics in human populations. To address these questions, we designed a synthetic persuasion dialogue scenario on the topic of climate change, where a 'convincer' agent generates a persuasive argument for a 'skeptic' agent, who subsequently assesses whether the argument changed its internal opinion state. Different types of arguments were generated to incorporate different linguistic dimensions underpinning psycho-linguistic theories of opinion change. We then asked human judges to evaluate the persuasiveness of machine-generated arguments. Arguments that included factual knowledge, markers of trust, expressions of support, and conveyed status were deemed most effective according to both humans and agents, with humans reporting a marked preference for knowledge-based arguments. Our experimental framework lays the groundwork for future in-silico studies of opinion dynamics, and our findings suggest that artificial agents have the potential of playing an important role in collective processes of opinion formation in online social media.
Making Large Language Models A Better Foundation For Dense Retrieval
Authors: Chaofan Li, Zheng Liu, Shitao Xiao, Yingxia Shao
Date: 2023-12-24 00:00:00
Tasks: Retrieval, Sentence, Text Generation

Dense retrieval needs to learn discriminative text embeddings to represent the semantic relationship between query and document. It may benefit from the using of large language models (LLMs), given LLMs' strong capability on semantic understanding. However, the LLMs are pre-trained by text generation tasks, whose working pattern is completely different from representing texts as embeddings. As a result, it is imperative to study how to adapt LLMs properly so that they can be effectively initialized as the backbone encoder for dense retrieval. In this paper, we propose a novel approach, called LLaRA (LLM adapted for dense RetrievAl), which works as a post-hoc adaptation of LLM for the dense retrieval application. LLaRA consists of two pretext tasks: EBAE (Embedding-Based Auto-Encoding) and EBAR (Embedding-Based Auto-Regression), where the text embeddings from LLM are used to reconstruct the tokens for the input sentence and predict the tokens for the next sentence, respectively. LLaRA turns out to be simple, lightweight, and highly effective. It is applied to adapt LLaMA-2-7B (base) on the Wikipedia corpus, where it substantially improves the model's fine-tuned performances on a variety of dense retrieval benchmarks, like MSMARCO and BEIR. Our model and code will be made publicly available at BGE repository.
A Group Fairness Lens for Large Language Models
Authors: Guanqun Bi, Lei Shen, Yuqiang Xie, Yanan Cao, Tiangang Zhu, Xiaodong He
Date: 2023-12-24 00:00:00
Tasks: Attribute, Fairness, Text Generation

The rapid advancement of large language models has revolutionized various applications but also raised crucial concerns about their potential to perpetuate biases and unfairness when deployed in social media contexts. Evaluating LLMs' potential biases and fairness has become crucial, as existing methods rely on limited prompts focusing on just a few groups, lacking a comprehensive categorical perspective. In this paper, we propose evaluating LLM biases from a group fairness lens using a novel hierarchical schema characterizing diverse social groups. Specifically, we construct a dataset, GFair, encapsulating target-attribute combinations across multiple dimensions. In addition, we introduce statement organization, a new open-ended text generation task, to uncover complex biases in LLMs. Extensive evaluations of popular LLMs reveal inherent safety concerns. To mitigate the biases of LLM from a group fairness perspective, we pioneer a novel chain-of-thought method GF-Think to mitigate biases of LLMs from a group fairness perspective. Experimental results demonstrate its efficacy in mitigating bias in LLMs to achieve fairness.
Towards Consistent Language Models Using Declarative Constraints
Authors: Jasmin Mousavi, Arash Termehchy
Date: 2023-12-24 00:00:00
Tasks: Management

Large language models have shown unprecedented abilities in generating linguistically coherent and syntactically correct natural language output. However, they often return incorrect and inconsistent answers to input questions. Due to the complexity and uninterpretability of the internally learned representations, it is challenging to modify language models such that they provide correct and consistent results. The data management community has developed various methods and tools for providing consistent answers over inconsistent datasets. In these methods, users specify the desired properties of data in a domain in the form of high-level declarative constraints. This approach has provided usable and scalable methods to delivering consistent information from inconsistent datasets. We aim to build upon this success and leverage these methods to modify language models such that they deliver consistent and accurate results. We investigate the challenges of using these ideas to obtain consistent and relevant answers from language models and report some preliminary empirical studies.
A Comprehensive Analysis of the Effectiveness of Large Language Models as Automatic Dialogue Evaluators
Authors: Chen Zhang, Luis Fernando D'Haro, Yiming Chen, Malu Zhang, Haizhou Li
Date: 2023-12-24 00:00:00
Tasks: Dialogue Evaluation

Automatic evaluation is an integral aspect of dialogue system research. The traditional reference-based NLG metrics are generally found to be unsuitable for dialogue assessment. Consequently, recent studies have suggested various unique, reference-free neural metrics that better align with human evaluations. Notably among them, large language models (LLMs), particularly the instruction-tuned variants like ChatGPT, are shown to be promising substitutes for human judges. Yet, existing works on utilizing LLMs for automatic dialogue evaluation are limited in their scope in terms of the number of meta-evaluation datasets, mode of evaluation, coverage of LLMs, etc. Hence, it remains inconclusive how effective these LLMs are. To this end, we conduct a comprehensive study on the application of LLMs for automatic dialogue evaluation. Specifically, we analyze the multi-dimensional evaluation capability of 30 recently emerged LLMs at both turn and dialogue levels, using a comprehensive set of 12 meta-evaluation datasets. Additionally, we probe the robustness of the LLMs in handling various adversarial perturbations at both turn and dialogue levels. Finally, we explore how model-level and dimension-level ensembles impact the evaluation performance. All resources are available at https://github.com/e0397123/comp-analysis.
Greedy Grammar Induction with Indirect Negative Evidence
Authors: Joseph Potashnik
Date: 2023-12-23 00:00:00
Tasks: LEMMA, Position

This paper offers a fresh look at the pumping lemma constant as an upper bound on the information required for learning Context Free Grammars. An objective function based on indirect negative evidence considers the occurrences, and non-occurrences, of a finite number of strings, encountered after a sufficiently long presentation. This function has optimal substructure in the hypotheses space, giving rise to a greedy search learner in a branch and bound method. A hierarchy of learnable classes is defined in terms of the number of production rules that must be added to interim solutions in order to incrementally fit the input. Efficiency strongly depends on the position of the target grammar in the hierarchy and on the richness of the input.
Paralinguistics-Enhanced Large Language Modeling of Spoken Dialogue
Authors: Guan-Ting Lin, Prashanth Gurunath Shivakumar, Ankur Gandhe, Chao-Han Huck Yang, Yile Gu, Shalini Ghosh, Andreas Stolcke, Hung-Yi Lee, Ivan Bulyko
Date: 2023-12-23 00:00:00
Tasks: Attribute, Language Modeling, Language Modelling, Question Answering, Sentiment Analysis, Sentiment Classification, Text Generation

Large Language Models (LLMs) have demonstrated superior abilities in tasks such as chatting, reasoning, and question-answering. However, standard LLMs may ignore crucial paralinguistic information, such as sentiment, emotion, and speaking style, which are essential for achieving natural, human-like spoken conversation, especially when such information is conveyed by acoustic cues. We therefore propose Paralinguistics-enhanced Generative Pretrained Transformer (ParalinGPT), an LLM that utilizes text and speech modalities to better model the linguistic content and paralinguistic attributes of spoken dialogue. The model takes the conversational context of text, speech embeddings, and paralinguistic attributes as input prompts within a serialized multitasking multimodal framework. Specifically, our framework serializes tasks in the order of current paralinguistic attribute prediction, response paralinguistic attribute prediction, and response text generation with autoregressive conditioning. We utilize the Switchboard-1 corpus, including its sentiment labels as the paralinguistic attribute, as our spoken dialogue dataset. Experimental results indicate the proposed serialized multitasking method outperforms typical sequence classification techniques on current and response sentiment classification. Furthermore, leveraging conversational context and speech embeddings significantly improves both response text generation and sentiment prediction. Our proposed framework achieves relative improvements of 6.7%, 12.0%, and 3.5% in current sentiment accuracy, response sentiment accuracy, and response text BLEU score, respectively.