ymoslem/acl-abstracts huggingface dataset:
Discriminative Language Model as Semantic Consistency Scorer for Prompt-based Few-Shot Text Classification (2024)

A successful prompt-based finetuning method should have three prerequisites: task compatibility, input compatibility, and evidence abundance. Bearing this belief in mind, this paper designs a novel prompt-based method (called DLM-SCS) for few-shot text classification, which utilizes the discriminative language model ELECTRA that is pretrained to distinguish whether a token is original or replaced. The method is built upon the intuitive idea that the prompt instantiated with the true label should have higher semantic consistency score than other prompts with false labels. Since a prompt usually consists of several components (or parts), its semantic consistency can be decomposed accordingly, which means each part can provide information for semantic consistency discrimination. The semantic consistency of each component is then computed by making use of the pretrained ELECTRA model, where no extra parameters get introduced. Extensive experiments have shown that our model outperforms several state-of-the-art prompt-based few-shot methods on 10 widely-used text classification tasks.
Disentangling Pretrained Representation to Leverage Low-Resource Languages in Multilingual Machine Translation (2024)

Multilingual neural machine translation aims to encapsulate multiple languages into a single model. However, it requires an enormous dataset, leaving the low-resource language (LRL) underdeveloped. As LRLs may benefit from shared knowledge of multilingual representation, we aspire to find effective ways to integrate unseen languages in a pre-trained model. Nevertheless, the intricacy of shared representation among languages hinders its full utilisation. To resolve this problem, we employed target language prediction and a central language-aware layer to improve representation in integrating LRLs. Focusing on improving LRLs in the linguistically diverse country of Indonesia, we evaluated five languages using a parallel corpus of 1,000 instances each, with experimental results measured by BLEU showing zero-shot improvement of 7.4 from the baseline score of 7.1 to a score of 15.5 at best. Further analysis showed that the gains in performance are attributed more to the disentanglement of multilingual representation in the encoder with the shift of the target language-specific representation in the decoder.
DISRPT: A Multilingual, Multi-domain, Cross-framework Benchmark for Discourse Processing (2024)

This paper presents DISRPT, a multilingual, multi-domain, and cross-framework benchmark dataset for discourse processing, covering the tasks of discourse unit segmentation, connective identification, and relation classification. DISRPT includes 13 languages, with data from 24 corpora covering about 4 millions tokens and around 250,000 discourse relation instances from 4 discourse frameworks: RST, SDRT, PDTB, and Discourse Dependencies. We present an overview of the data, its development across three NLP shared tasks on discourse processing carried out in the past five years, and the latest modifications and added extensions. We also carry out an evaluation of state-of-the-art multilingual systems trained on the data for each task, showing plateau performance on segmentation, but important room for improvement for connective identification and relation classification. The DISRPT benchmark employs a unified format that we make available on GitHub and HuggingFace in order to encourage future work on discourse processing across languages, domains, and frameworks.
Distantly Supervised Contrastive Learning for Low-Resource Scripting Language Summarization (2024)

Code summarization provides a natural language description for a given piece of code. In this work, we focus on scripting code{---}programming languages that interact with specific devices through commands. The low-resource nature of scripting languages makes traditional code summarization methods challenging to apply. To address this, we introduce a novel framework: distantly supervised contrastive learning for low-resource scripting language summarization. This framework leverages limited atomic commands and category constraints to enhance code representations. Extensive experiments demonstrate our method`s superiority over competitive baselines.
Distillation with Explanations from Large Language Models (2024)

Free-text explanations are crucial for enhancing the interpretability of AI models. However, training models to generate high-quality free-text explanations is challenging, primarily due to the requirement of a substantial amount of human-written explanations, which can be expensive. Recently, Large language models (LLMs) like ChatGPT and GPT-4 have made remarkable progress in various NLP tasks while also providing explanations alongside their answers. Leveraging LLMs for data labeling offers a more cost-effective alternative. However, a key concern arises from the fact that the answers provided by LLMs are not entirely accurate, potentially introducing noise to both task outputs and explanation generation. To remedy this, we propose a new mechanism, Distillation with Explanations from LLMs. we observe that despite the incorrectness in LLMs-generated answers, their explanations are consistent with their answers. Leveraging this consistency, our method combines the ground truth labels and answers-explanations generated by LLMs, to simultaneously generate more accurate answers and the corresponding free-text explanations. Experimental results demonstrate that our approach achieves improved predictive performance and also generates explanations that exhibit greater alignment with the model`s task outputs.
Distill, Fuse, Pre-train: Towards Effective Event Causality Identification with Commonsense-Aware Pre-trained Model (2024)

Event Causality Identification (ECI) aims to detect causal relations between events in unstructured texts. This task is challenged by the lack of data and explicit causal clues. Some methods incorporate explicit knowledge from external knowledge graphs (KGs) into Pre-trained Language Models (PLMs) to tackle these issues, achieving certain accomplishments. However, they ignore that existing KGs usually contain trivial knowledge which may prejudice the performance. Moreover, they simply integrate the concept triplets, underutilizing the deep interaction between the text and external graph. In this paper, we propose an effective pipeline DFP, i.e., Distill, Fuse and Pre-train, to build a commonsense-aware pre-trained model which integrates reliable task-specific knowledge from commonsense graphs. This pipeline works as follows: (1) To leverage the reliable knowledge, commonsense graph distillation is proposed to distill commonsense graphs and obtain the meta-graph which contain credible task-oriented knowledge. (2) To model the deep interaction between the text and external graph, heterogeneous information fusion is proposed to fuse them through a commonsense-aware memory network. (3) Continual pre-training designs three continual pre-training tasks to further align and fuse the text and the commonsense meta-graph. Through extensive experiments on two benchmarks, we demonstrate the validity of our pipeline.
Distilling Causal Effect of Data in Continual Few-shot Relation Learning (2024)

Continual Few-Shot Relation Learning (CFRL) aims to learn an increasing number of new relational patterns from a data stream. However, due to the limited number of samples and the continual training mode, this method frequently encounters the catastrophic forgetting issues. The research on causal inference suggests that this issue is caused by the loss of causal effects from old data during the new training process. Inspired by the causal graph, we propose a unified causal framework for CFRL to restore the causal effects. Specifically, we establish two additional causal paths from old data to predictions by having the new data and memory data collide with old data separately in the old feature space. This augmentation allows us to preserve causal effects effectively and enhance the utilization of valuable information within memory data, thereby alleviating the phenomenon of catastrophic forgetting. Furthermore, we introduce a self-adaptive weight to achieve a delicate balance of causal effects between the new and old relation types. Extensive experiments demonstrate the superiority of our method over existing state-of-the-art approaches in CFRL task settings. Our codes are publicly available at: https://github.com/ywh140/CECF.
Distractor Generation Using Generative and Discriminative Capabilities of Transformer-based Models (2024)

Multiple Choice Questions (MCQs) are very common in both high-stakes and low-stakes examinations, and their effectiveness in assessing students relies on the quality and diversity of distractors, which are the incorrect answer options provided alongside the correct answer. Motivated by the progress in generative language models, we propose a two-step automatic distractor generation approach which is based on text to text transfer transformer models. Unlike most of previous methods for distractor generation, our approach does not rely on the correct answer options. Instead, it first generates both correct and incorrect answer options, and then discriminates potential correct options from distractors. Identified distractors are finally categorised based on semantic similarity scores into separate clusters, and the cluster heads are selected as our final distinct distractors. Experiments on two publicly available datasets show that our approach outperforms previous models both in the case of single-word answer options and longer-sequence reading comprehension questions.
Distribution Aware Metrics for Conditional Natural Language Generation (2024)

Traditional automated metrics for evaluating conditional natural language generation rely on pairwise comparisons between a single generated text and the best-matching gold-standard reference. This method is effective when ground truth data diversity can be attributed to noise, however, it falls short when diversity in references holds valuable contextual information, as in visual description or summarization, as it does not evaluate the ability of a model to generate text matching the diversity of the ground truth samples. In this paper, we challenge the adequacy of existing metrics in such semantically diverse contexts and introduce a novel approach for evaluating conditional language generation models, leveraging a family of meta-metrics that build on existing pairwise distance functions. These meta-metrics assess not just single-samples, but distributions of reference and model-generated captions using small sample sets. We demonstrate our approach through a case study of visual description in the English language which reveals not only how current models prioritize single-description quality over diversity, but further sheds light on the impact of sampling methods and temperature settings on description quality and diversity.
Diversifying Question Generation over Knowledge Base via External Natural Questions (2024)

Previous methods on knowledge base question generation (KBQG) primarily focus on refining the quality of a single generated question. However, considering the remarkable paraphrasing ability of humans, we believe that diverse texts can express identical semantics through varied expressions. The above insights make diversifying question generation an intriguing task, where the first challenge is evaluation metrics for diversity. Current metrics inadequately assess the aforementioned diversity. They calculate the ratio of unique n-grams in the generated question, which tends to measure duplication rather than true diversity. Accordingly, we devise a new diversity evaluation metric, which measures the diversity among top-k generated questions for each instance while ensuring their relevance to the ground truth. Clearly, the second challenge is how to enhance diversifying question generation. To address this challenge, we introduce a dual model framework interwoven by two selection strategies to generate diverse questions leveraging external natural questions. The main idea of our dual framework is to extract more diverse expressions and integrate them into the generation model to enhance diversifying question generation. Extensive experiments on widely used benchmarks for KBQG show that our approach can outperform pre-trained language model baselines and text-davinci-003 in diversity while achieving comparable performance with ChatGPT.
DMON: A Simple Yet Effective Approach for Argument Structure Learning (2024)

Argument structure learning (ASL) entails predicting relations between arguments. Because it can structure a document to facilitate its understanding, it has been widely applied in many fields (medical, commercial, and scientific domains). Despite its broad utilization, ASL remains a challenging task because it involves examining the complex relationships between the sentences in a potentially unstructured discourse. To resolve this problem, we have developed a simple yet effective approach called Dual-tower Multi-scale cOnvolution neural Network (DMON) for the ASL task. Specifically, we organize arguments into a relationship matrix that together with the argument embeddings forms a relationship tensor and design a mechanism to capture relations with contextual arguments. Experimental results on three different-domain argument mining datasets demonstrate that our framework outperforms state-of-the-art models. We will release the code after paper acceptance.
Doc2SoarGraph: Discrete Reasoning over Visually-Rich Table-Text Documents via Semantic-Oriented Hierarchical Graphs (2024)

Table-text document (e.g., financial reports) understanding has attracted increasing attention in recent two years. TAT-DQA is a realistic setting for the understanding of visually-rich table-text documents, which involves answering associated questions requiring discrete reasoning. Most existing work relies on token-level semantics, falling short in the reasoning across document elements such as quantities and dates. To address this limitation, we propose a novel Doc2SoarGraph model that exploits element-level semantics and employs Semantic-oriented hierarchical Graph structures to capture the differences and correlations among different elements within the given document and question. Extensive experiments on the TAT-DQA dataset reveal that our model surpasses the state-of-the-art conventional method (i.e., MHST) and large language model (i.e., ChatGPT) by 17.73 and 6.49 points respectively in terms of Exact Match (EM) metric, demonstrating exceptional effectiveness.
DOC-RAG: ASR Language Model Personalization with Domain-Distributed Co-occurrence Retrieval Augmentation (2024)

We propose DOC-RAG - Domain-distributed Co-occurrence Retrieval Augmentation for ASR language model personalization aiming to improve the automatic speech recognition of rare word patterns in unseen domains. Our approach involves contrastively training a document retrieval module to rank external knowledge domains based on their semantic similarity with respect to the input query. We further use n-gram co-occurrence distribution to recognize rare word patterns associated with specific domains. We aggregate the next word probability distribution based on the relative importance of different domains. Extensive experiments on three user-specific speech-to-text tasks for meetings, TED talks, and financial earnings calls show that DOC-RAG significantly outperforms strong baselines with an 8-15{\%} improvement in terms of perplexity and a 4-7{\%} reduction in terms of Word Error Rates in various settings.
DocScript: Document-level Script Event Prediction (2024)

We present a novel task of document-level script event prediction, which aims to predict the next event given a candidate list of narrative events in long-form documents. To enable this, we introduce DocSEP, a challenging dataset in two new domains - contractual documents and Wikipedia articles, where timeline events may be paragraphs apart and may require multi-hop temporal and causal reasoning. We benchmark existing baselines and present a novel architecture called DocScript to learn sequential ordering between events at the document scale. Our experimental results on the DocSEP dataset demonstrate that learning longer-range dependencies between events is a key challenge and show that contemporary LLMs such as ChatGPT and FlanT5 struggle to solve this task, indicating their lack of reasoning abilities for understanding causal relationships and temporal sequences within long texts.
Document-Level Event Extraction via Information Interaction Based on Event Relation and Argument Correlation (2024)

Document-level Event Extraction (DEE) is a vital task in NLP as it seeks to automatically recognize and extract event information from a document. However, current approaches often overlook intricate relationships among events and subtle correlations among arguments within a document, which can significantly impact the effectiveness of event type recognition and the extraction of cross-sentence arguments in DEE task. This paper proposes a novel Correlation Association Interactive Network (CAINet), comprising two key components: event relationship graph and argument correlation graph. In particular, the event relationship graph models the relationship among various events through structural associations among event nodes and sentence nodes, to improve the accuracy of event recognition. On the other hand, the arguments correlation graph models the correlations among arguments by quantifying the strength of association among arguments, to effectively aggregate cross-sentence arguments, contributing to the overall success of DEE. Furthermore, we use the large language model to execute DEE task experiments. Experimental results show the proposed CAINet outperforms existing state-of-the-art models and large language models in terms of F1-score across two benchmark datasets.
Document Set Expansion with Positive-Unlabeled Learning Using Intractable Density Estimation (2024)

The Document Set Expansion (DSE) task involves identifying relevant documents from large collections based on a limited set of example documents. Previous research has highlighted Positive and Unlabeled (PU) learning as a promising approach for this task. However, most PU methods rely on the unrealistic assumption of knowing the class prior for positive samples in the collection. To address this limitation, this paper introduces a novel PU learning framework that utilizes intractable density estimation models. Experiments conducted on PubMed and Covid datasets in a transductive setting showcase the effectiveness of the proposed method for DSE. Code is available from https://github.com/Beautifuldog01/Document-set-expansion-puDE.
Do Emergent Abilities Exist in Quantized Large Language Models: An Empirical Study (2024)

Despite the superior performance, Large Language Models (LLMs) require significant computational resources for deployment and use. To overcome this issue, quantization methods have been widely applied to reduce the memory footprint of LLMs as well as increase the inference rate. However, a major challenge is that low-bit quantization methods often lead to performance degradation. It is important to understand how quantization impacts the capacity of LLMs. Different from previous studies focused on overall performance, this work aims to investigate the impact of quantization on \textit{emergent abilities}, which are important characteristics that distinguish LLMs from small language models. Specifically, we examine the abilities of in-context learning, chain-of-thought reasoning, and instruction-following in quantized LLMs. Our empirical experiments show that these emergent abilities still exist in 4-bit quantization models, while 2-bit models encounter severe performance degradation on the test of these abilities. To improve the performance of low-bit models, we conduct two special experiments: (1) fine-gained impact analysis that studies which components (or substructures) are more sensitive to quantization, and (2) performance compensation through model fine-tuning. Our work derives a series of important findings to understand the impact of quantization on emergent abilities and sheds light on the possibilities of extremely low-bit quantization for LLMs.
Does ChatGPT Know That It Does Not Know? Evaluating the Black-Box Calibration of ChatGPT (2024)

Recently, ChatGPT has demonstrated remarkable performance in various downstream tasks such as open-domain question answering, machine translation, and code generation. As a general-purpose task solver, an intriguing inquiry arises: Does ChatGPT itself know that it does not know, without any access to internal states? In response to this query, we present an initial evaluation of ChatGPT for black-box calibration. We designed three types of proxy confidence, from three perspectives to assess its performance. Experiments are conducted on five datasets, spanning four tasks, and the results show that ChatGPT has a degree of capability for black-box calibration. Specifically, proxy confidence displayed a significantly positive Pearson correlation (95.16{\%}) with accuracy in the TruthfulQA dataset, while revealing a negative correlation in the ModAr dataset. We delved deeper into ChatGPT`s black-box calibration ability by examining failure cases in the ModAr dataset. Our analysis revealed that ChatGPT`s tendency to exhibit overconfidence may stem from its reliance on semantic priors. Furthermore, we investigated why ChatGPT performs relatively well in TruthfulQA. The findings suggest that ChatGPT might implicitly acquire calibration skills during the reinforcement learning process, rather than relying solely on simplistic heuristics.
Does the Generator Mind Its Contexts? An Analysis of Generative Model Faithfulness under Context Transfer (2024)

he present study introduces the knowledge-augmented generator, which is specifically designed to produce information that remains grounded in contextual knowledge, regardless of alterations in the context. Previous research has predominantly focused on examining hallucinations stemming from static input, such as in the domains of summarization or machine translation. However, our investigation delves into the faithfulness of generative question answering in the presence of dynamic knowledge. Our objective is to explore the existence of hallucinations arising from parametric memory when contextual knowledge undergoes changes, while also analyzing the underlying causes for their occurrence. In order to efficiently address this issue, we propose a straightforward yet effective measure for detecting such hallucinations. Intriguingly, our investigation uncovers that all models exhibit a tendency to generate previous answers as hallucinations. To gain deeper insights into the underlying causes of this phenomenon, we conduct a series of experiments that verify the critical role played by context in hallucination, both during training and testing, from various perspectives.