pwc-archive/papers-with-abstracts huggingface dataset:
Context-Enhanced Language Models for Generating Multi-Paper Citations
Authors: Avinash Anand, Kritarth Prasad, Ujjwal Goel, Mohit Gupta, Naman Lal, Astha Verma, Rajiv Ratn Shah
Date: 2024-04-22 00:00:00
Tasks: Knowledge Graphs, Sentence, Text Generation

Citation text plays a pivotal role in elucidating the connection between scientific documents, demanding an in-depth comprehension of the cited paper. Constructing citations is often time-consuming, requiring researchers to delve into extensive literature and grapple with articulating relevant content. To address this challenge, the field of citation text generation (CTG) has emerged. However, while earlier methods have primarily centered on creating single-sentence citations, practical scenarios frequently necessitate citing multiple papers within a single paragraph. To bridge this gap, we propose a method that leverages Large Language Models (LLMs) to generate multi-citation sentences. Our approach involves a single source paper and a collection of target papers, culminating in a coherent paragraph containing multi-sentence citation text. Furthermore, we introduce a curated dataset named MCG-S2ORC, composed of English-language academic research papers in Computer Science, showcasing multiple citation instances. In our experiments, we evaluate three LLMs LLaMA, Alpaca, and Vicuna to ascertain the most effective model for this endeavor. Additionally, we exhibit enhanced performance by integrating knowledge graphs from target papers into the prompts for generating citation text. This research underscores the potential of harnessing LLMs for citation generation, opening a compelling avenue for exploring the intricate connections between scientific documents.
Understanding the role of FFNs in driving multilingual behaviour in LLMs
Authors: Sunit Bhattacharya, Ondřej Bojar
Date: 2024-04-22 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Multilingualism in Large Language Models (LLMs) is an yet under-explored area. In this paper, we conduct an in-depth analysis of the multilingual capabilities of a family of a Large Language Model, examining its architecture, activation patterns, and processing mechanisms across languages. We introduce novel metrics to probe the model's multilingual behaviour at different layers and shed light on the impact of architectural choices on multilingual processing. Our findings reveal different patterns of multilinugal processing in the sublayers of Feed-Forward Networks of the models. Furthermore, we uncover the phenomenon of "over-layerization" in certain model configurations, where increasing layer depth without corresponding adjustments to other parameters may degrade model performance. Through comparisons within and across languages, we demonstrate the interplay between model architecture, layer depth, and multilingual processing capabilities of LLMs trained on multiple languages.
Lightweight Connective Detection Using Gradient Boosting
Authors: Mustafa Erolcan Er, Murathan Kurfali, Deniz Zeyrek
Date: 2024-04-21 00:00:00
Tasks: Connective Detection, CPU

In this work, we introduce a lightweight discourse connective detection system. Employing gradient boosting trained on straightforward, low-complexity features, this proposed approach sidesteps the computational demands of the current approaches that rely on deep neural networks. Considering its simplicity, our approach achieves competitive results while offering significant gains in terms of time even on CPU. Furthermore, the stable performance across two unrelated languages suggests the robustness of our system in the multilingual scenario. The model is designed to support the annotation of discourse relations, particularly in scenarios with limited resources, while minimizing performance loss.
Evaluating Retrieval Quality in Retrieval-Augmented Generation
Authors: Alireza Salemi, Hamed Zamani
Date: 2024-04-21 00:00:00
Tasks: GPU, Language Modeling, Language Modelling, Large Language Model, RAG, Retrieval, Retrieval-augmented Generation

Evaluating retrieval-augmented generation (RAG) presents challenges, particularly for retrieval models within these systems. Traditional end-to-end evaluation methods are computationally expensive. Furthermore, evaluation of the retrieval model's performance based on query-document relevance labels shows a small correlation with the RAG system's downstream performance. We propose a novel evaluation approach, eRAG, where each document in the retrieval list is individually utilized by the large language model within the RAG system. The output generated for each document is then evaluated based on the downstream task ground truth labels. In this manner, the downstream performance for each document serves as its relevance label. We employ various downstream task metrics to obtain document-level annotations and aggregate them using set-based or ranking metrics. Extensive experiments on a wide range of datasets demonstrate that eRAG achieves a higher correlation with downstream RAG performance compared to baseline methods, with improvements in Kendall's $\tau$ correlation ranging from 0.168 to 0.494. Additionally, eRAG offers significant computational advantages, improving runtime and consuming up to 50 times less GPU memory than end-to-end evaluation.
Using Adaptive Empathetic Responses for Teaching English
Authors: Li Siyan, Teresa Shao, Zhou Yu, Julia Hirschberg
Date: 2024-04-21 00:00:00
Tasks: Chatbot

Existing English-teaching chatbots rarely incorporate empathy explicitly in their feedback, but empathetic feedback could help keep students engaged and reduce learner anxiety. Toward this end, we propose the task of negative emotion detection via audio, for recognizing empathetic feedback opportunities in language learning. We then build the first spoken English-teaching chatbot with adaptive, empathetic feedback. This feedback is synthesized through automatic prompt optimization of ChatGPT and is evaluated with English learners. We demonstrate the effectiveness of our system through a preliminary user study.
How to Encode Domain Information in Relation Classification
Authors: Elisa Bassignana, Viggo Unmack Gascou, Frida Nøhr Laustsen, Gustav Kristensen, Marie Haahr Petersen, Rob van der Goot, Barbara Plank
Date: 2024-04-21 00:00:00
Tasks: Classification, Relation, Relation Classification

Current language models require a lot of training data to obtain high performance. For Relation Classification (RC), many datasets are domain-specific, so combining datasets to obtain better performance is non-trivial. We explore a multi-domain training setup for RC, and attempt to improve performance by encoding domain information. Our proposed models improve > 2 Macro-F1 against the baseline setup, and our analysis reveals that not all the labels benefit the same: The classes which occupy a similar space across domains (i.e., their interpretation is close across them, for example "physical") benefit the least, while domain-dependent relations (e.g., "part-of'') improve the most when encoding domain information.
Embarrassingly Simple Unsupervised Aspect Based Sentiment Tuple Extraction
Authors: Kevin Scaria, Abyn Scaria, Ben Scaria
Date: 2024-04-21 00:00:00
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sentence, Sentiment Analysis

Aspect Based Sentiment Analysis (ABSA) tasks involve the extraction of fine-grained sentiment tuples from sentences, aiming to discern the author's opinions. Conventional methodologies predominantly rely on supervised approaches; however, the efficacy of such methods diminishes in low-resource domains lacking labeled datasets since they often lack the ability to generalize across domains. To address this challenge, we propose a simple and novel unsupervised approach to extract opinion terms and the corresponding sentiment polarity for aspect terms in a sentence. Our experimental evaluations, conducted on four benchmark datasets, demonstrate compelling performance to extract the aspect oriented opinion words as well as assigning sentiment polarity. Additionally, unsupervised approaches for opinion word mining have not been explored and our work establishes a benchmark for the same.
Trojan Detection in Large Language Models: Insights from The Trojan Detection Challenge
Authors: Narek Maloyan, Ekansh Verma, Bulat Nutfullin, Bislan Ashinov
Date: 2024-04-21 00:00:00

Large Language Models (LLMs) have demonstrated remarkable capabilities in various domains, but their vulnerability to trojan or backdoor attacks poses significant security risks. This paper explores the challenges and insights gained from the Trojan Detection Competition 2023 (TDC2023), which focused on identifying and evaluating trojan attacks on LLMs. We investigate the difficulty of distinguishing between intended and unintended triggers, as well as the feasibility of reverse engineering trojans in real-world scenarios. Our comparative analysis of various trojan detection methods reveals that achieving high Recall scores is significantly more challenging than obtaining high Reverse-Engineering Attack Success Rate (REASR) scores. The top-performing methods in the competition achieved Recall scores around 0.16, comparable to a simple baseline of randomly sampling sentences from a distribution similar to the given training prefixes. This finding raises questions about the detectability and recoverability of trojans inserted into the model, given only the harmful targets. Despite the inability to fully solve the problem, the competition has led to interesting observations about the viability of trojan detection and improved techniques for optimizing LLM input prompts. The phenomenon of unintended triggers and the difficulty in distinguishing them from intended triggers highlights the need for further research into the robustness and interpretability of LLMs. The TDC2023 has provided valuable insights into the challenges and opportunities associated with trojan detection in LLMs, laying the groundwork for future research in this area to ensure their safety and reliability in real-world applications.
PEACH: Pretrained-embedding Explanation Across Contextual and Hierarchical Structure
Authors: Feiqi Cao, Caren Han, Hyunsuk Chung
Date: 2024-04-21 00:00:00
Tasks: Attribute, feature selection, text-classification, Text Classification

In this work, we propose a novel tree-based explanation technique, PEACH (Pretrained-embedding Explanation Across Contextual and Hierarchical Structure), that can explain how text-based documents are classified by using any pretrained contextual embeddings in a tree-based human-interpretable manner. Note that PEACH can adopt any contextual embeddings of the PLMs as a training input for the decision tree. Using the proposed PEACH, we perform a comprehensive analysis of several contextual embeddings on nine different NLP text classification benchmarks. This analysis demonstrates the flexibility of the model by applying several PLM contextual embeddings, its attribute selections, scaling, and clustering methods. Furthermore, we show the utility of explanations by visualising the feature selection and important trend of text classification via human-interpretable word-cloud-based trees, which clearly identify model mistakes and assist in dataset debugging. Besides interpretability, PEACH outperforms or is similar to those from pretrained models.
Incorporating Different Verbal Cues to Improve Text-Based Computer-Delivered Health Messaging
Authors: Samuel Rhys Cox
Date: 2024-04-21 00:00:00
Tasks: Chatbot

The ubiquity of smartphones has led to an increase in on demand healthcare being supplied. For example, people can share their illness-related experiences with others similar to themselves, and healthcare experts can offer advice for better treatment and care for remediable, terminal and mental illnesses. As well as this human-to-human communication, there has been an increased use of human-to-computer digital health messaging, such as chatbots. These can prove advantageous as they offer synchronous and anonymous feedback without the need for a human conversational partner. However, there are many subtleties involved in human conversation that a computer agent may not properly exhibit. For example, there are various conversational styles, etiquettes, politeness strategies or empathic responses that need to be chosen appropriately for the conversation. Encouragingly, computers are social actors (CASA) posits that people apply the same social norms to computers as they would do to people. On from this, previous studies have focused on applying conversational strategies to computer agents to make them embody more favourable human characteristics. However, if a computer agent fails in this regard it can lead to negative reactions from users. Therefore, in this dissertation we describe a series of studies we carried out to lead to more effective human-to-computer digital health messaging. In our first study, we use the crowd [...] Our second study investigates the effect of a health chatbot's conversational style [...] In our final study, we investigate the format used by a chatbot when [...] In summary, we have researched how to create more effective digital health interventions starting from generating health messages, to choosing an appropriate formality of messaging, and finally to formatting messages which reference a user's previous utterances.
"A good pun is its own reword": Can Large Language Models Understand Puns?
Authors: Zhijun Xu, Siyu Yuan, Lingjie Chen, Deqing Yang
Date: 2024-04-21 00:00:00
Tasks: In-Context Learning

Puns play a vital role in academic research due to their distinct structure and clear definition, which aid in the comprehensive analysis of linguistic humor. However, the understanding of puns in large language models (LLMs) has not been thoroughly examined, limiting their use in creative writing and humor creation. In this paper, we leverage three popular tasks, i.e., pun recognition, explanation and generation to systematically evaluate the capabilities of LLMs in pun understanding. In addition to adopting the automated evaluation metrics from prior research, we introduce new evaluation methods and metrics that are better suited to the in-context learning paradigm of LLMs. These new metrics offer a more rigorous assessment of an LLM's ability to understand puns and align more closely with human cognition than previous metrics. Our findings reveal the "lazy pun generation" pattern and identify the primary challenges LLMs encounter in understanding puns.
ChatRetriever: Adapting Large Language Models for Generalized and Robust Conversational Dense Retrieval
Authors: Kelong Mao, Chenlong Deng, Haonan Chen, Fengran Mo, Zheng Liu, Tetsuya Sakai, Zhicheng Dou
Date: 2024-04-21 00:00:00
Tasks: Contrastive Learning, Conversational Search, Retrieval

Conversational search requires accurate interpretation of user intent from complex multi-turn contexts. This paper presents ChatRetriever, which inherits the strong generalization capability of large language models to robustly represent complex conversational sessions for dense retrieval. To achieve this, we propose a simple and effective dual-learning approach that adapts LLM for retrieval via contrastive learning while enhancing the complex session understanding through masked instruction tuning on high-quality conversational instruction tuning data. Extensive experiments on five conversational search benchmarks demonstrate that ChatRetriever substantially outperforms existing conversational dense retrievers, achieving state-of-the-art performance on par with LLM-based rewriting approaches. Furthermore, ChatRetriever exhibits superior robustness in handling diverse conversational contexts. Our work highlights the potential of adapting LLMs for retrieval with complex inputs like conversational search sessions and proposes an effective approach to advance this research direction.
E-QGen: Educational Lecture Abstract-based Question Generation System
Authors: Mao-Siang Chen, An-Zi Yen
Date: 2024-04-21 00:00:00
Tasks: Question Generation, Question-Generation

To optimize the preparation process for educators in academic lectures and associated question-and-answer sessions, this paper presents E-QGen, a lecture abstract-based question generation system. Given a lecture abstract, E-QGen generates potential student inquiries. The questions suggested by our system are expected to not only facilitate teachers in preparing answers in advance but also enable them to supply additional resources when necessary.
IMO: Greedy Layer-Wise Sparse Representation Learning for Out-of-Distribution Text Classification with Pre-trained Models
Authors: Tao Feng, Lizhen Qu, Zhuang Li, Haolan Zhan, Yuncheng Hua, Gholamreza Haffari
Date: 2024-04-21 00:00:00
Tasks: Domain Generalization, Representation Learning, text-classification, Text Classification

Machine learning models have made incredible progress, but they still struggle when applied to examples from unseen domains. This study focuses on a specific problem of domain generalization, where a model is trained on one source domain and tested on multiple target domains that are unseen during training. We propose IMO: Invariant features Masks for Out-of-Distribution text classification, to achieve OOD generalization by learning invariant features. During training, IMO would learn sparse mask layers to remove irrelevant features for prediction, where the remaining features keep invariant. Additionally, IMO has an attention module at the token level to focus on tokens that are useful for prediction. Our comprehensive experiments show that IMO substantially outperforms strong baselines in terms of various evaluation metrics and settings.
Fine-Grained Named Entities for Corona News
Authors: Sefika Efeoglu, Adrian Paschke
Date: 2024-04-20 00:00:00
Tasks: Articles, named-entity-recognition, Named Entity Recognition, Relation Extraction

Information resources such as newspapers have produced unstructured text data in various languages related to the corona outbreak since December 2019. Analyzing these unstructured texts is time-consuming without representing them in a structured format; therefore, representing them in a structured format is crucial. An information extraction pipeline with essential tasks -- named entity tagging and relation extraction -- to accomplish this goal might be applied to these texts. This study proposes a data annotation pipeline to generate training data from corona news articles, including generic and domain-specific entities. Named entity recognition models are trained on this annotated corpus and then evaluated on test sentences manually annotated by domain experts evaluating the performance of a trained model. The code base and demonstration are available at https://github.com/sefeoglu/coronanews-ner.git.
Explanation based Bias Decoupling Regularization for Natural Language Inference
Authors: Jianxiang Zang, Hui Liu
Date: 2024-04-20 00:00:00
Tasks: Natural Language Inference

The robustness of Transformer-based Natural Language Inference encoders is frequently compromised as they tend to rely more on dataset biases than on the intended task-relevant features. Recent studies have attempted to mitigate this by reducing the weight of biased samples during the training process. However, these debiasing methods primarily focus on identifying which samples are biased without explicitly determining the biased components within each case. This limitation restricts those methods' capability in out-of-distribution inference. To address this issue, we aim to train models to adopt the logic humans use in explaining causality. We propose a simple, comprehensive, and interpretable method: Explanation based Bias Decoupling Regularization (EBD-Reg). EBD-Reg employs human explanations as criteria, guiding the encoder to establish a tripartite parallel supervision of Distinguishing, Decoupling and Aligning. This method enables encoders to identify and focus on keywords that represent the task-relevant features during inference, while discarding the residual elements acting as biases. Empirical evidence underscores that EBD-Reg effectively guides various Transformer-based encoders to decouple biases through a human-centric lens, significantly surpassing other methods in terms of out-of-distribution inference capabilities.
Swa Bhasha: Message-Based Singlish to Sinhala Transliteration
Authors: Maneesha U. Athukorala, Deshan K. Sumanathilaka
Date: 2024-04-20 00:00:00
Tasks: Transliteration

Machine Transliteration provides the ability to transliterate a basic language into different languages in a computational way. Transliteration is an important technical process that has caught the attention most recently. The Sinhala transliteration has many constraints because of the insufficiency of resources in the Sinhala language. Due to these limitations, Sinhala Transliteration is highly complex and time-consuming. Therefore, the majority of the Sri Lankans uses non-formal texting language named 'Singlish' to make that process simple. This study has focused on the transliteration of the Singlish language at the word level by reducing the complication in the transliteration. A new approach of coding system has invented with the rule-based approach that can map the matching Sinhala words even without the vowels. Various typing patterns were collected by different communities for this. The collected data have analyzed with every Sinhala character and unique Singlish patterns related to them were generated. The system has introduced a newly initiated numeric coding system to use with the Singlish letters by matching with the recognized typing patterns. For the mapping process, fuzzy logic-based implementation has used. A codified dictionary has also implemented including unique numeric values. In this system, Each Romanized English letter was assigned with a unique numeric code that can construct a unique pattern for each word. The system can identify the most relevant Sinhala word that matches with the pattern of the Singlish word or it gives the most related word suggestions. For example, the word 'kiyanna,kianna, kynna, kynn, kiynna' have mapped with the accurate Sinhala word "kiyanna". These results revealed that the 'Swa Bhasha' transliteration system has the ability to enhance the Sinhala users' experience while conducting the texting in Singlish to Sinhala.