GenAI4ELab/papercli-papers huggingface dataset:
Transformer-Based Language Model Surprisal Predicts Human Reading Times Best with About Two Billion Training Tokens
Authors: Byung-Doh Oh; William Schuler
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Recent psycholinguistic studies have drawn conflicting conclusions about the relationship between the quality of a language model and the ability of its surprisal estimates to predict human reading times, which has been speculated to be due to the large gap in both the amount of training data and model capacity across studies. The current work aims to consolidate these findings by evaluating surprisal estimates from Transformer-based language model variants that vary systematically in the amount of training data and model capacity on their ability to predict human reading times. The results show that surprisal estimates from most variants with contemporary model capacities provide the best fit after seeing about two billion training tokens, after which they begin to diverge from humanlike expectations. Additionally, newly-trained smaller model variants reveal a ‘tipping point’ at convergence, after which the decrease in language model perplexity begins to result in poorer fits to human reading times. These results suggest that the massive amount of training data is mainly responsible for the poorer fit achieved by surprisal from larger pre-trained language models, and that a certain degree of model capacity is necessary for Transformer-based language models to capture humanlike expectations.
ExplainCPE: A Free-text Explanation Benchmark of Chinese Pharmacist Examination
Authors: Dongfang Li; Jindi Yu; Baotian Hu; Zhenran Xu; Min Zhang
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: In the field of Large Language Models (LLMs), researchers are increasingly exploring their effectiveness across a wide range of tasks. However, a critical area that requires further investigation is the interpretability of these models, particularly the ability to generate rational explanations for their decisions. Most existing explanation datasets are limited to the English language and the general domain, which leads to a scarcity of linguistic diversity and a lack of resources in specialized domains, such as medical. To mitigate this, we propose ExplainCPE, a challenging medical dataset consisting of over 7K problems from Chinese Pharmacist Examination, specifically tailored to assess the model-generated explanations. From the overall results, only GPT-4 passes the pharmacist examination with a 75.7% accuracy, while other models like ChatGPT fail. Further detailed analysis of LLM-generated explanations reveals the limitations of LLMs in understanding medical text and executing computational reasoning. With the increasing importance of AI safety and trustworthiness, ExplainCPE takes a step towards improving and evaluating the interpretability of LLMs in the medical domain.
CLASS: A Design Framework for Building Intelligent Tutoring Systems Based on Learning Science principles
Authors: Shashank Sonkar; Naiming Liu; Debshila Mallick; Richard Baraniuk
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: We present a design framework called Conversational Learning with Analytical Step-by-Step Strategies (CLASS) for building advanced Intelligent Tutoring Systems (ITS) powered by high-performance Large Language Models (LLMs). The CLASS framework empowers ITS with two key capabilities. First, through a carefully curated scaffolding dataset, CLASS equips ITS with essential problem-solving strategies, enabling it to provide tutor-like, step-by-step guidance to students. Second, by using a dynamic conversational dataset, CLASS assists ITS in facilitating natural language interactions, fostering engaging student-tutor conversations. The CLASS framework also provides valuable insights into ITS’s internal decision-making process which allows seamless integration of user feedback, thus enabling continuous refinement and improvement. We also present a proof-of-concept ITS, referred to as SPOCK, which is trained using the CLASS framework with a focus on introductory college level biology content. A carefully constructed protocol was developed for SPOCK’s preliminary evaluation, examining aspects such as the factual accuracy and relevance of its responses. Experts in the field of biology offered favorable remarks, particularly highlighting SPOCK’s capability to break down questions into manageable subproblems and provide encouraging responses to students.
Normal-Abnormal Decoupling Memory for Medical Report Generation
Authors: Guosheng Zhao; Yan Yan; Zijian Zhao
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: The automatic generation of medical reports plays a crucial role in clinical automation. In contrast to natural images, radiological images exhibit a high degree of similarity, while medical data are prone to data bias and complex noise, posing challenges for existing methods in capturing nuanced visual information. To address these challenges, we introduce a novel normal-abnormal semantic decoupling network that utilizes abnormal pattern memory. Different from directly optimizing the network using medical reports, we optimize visual extraction through the extraction of abnormal semantics from the reports. Moreover, we independently learn normal semantics based on abnormal semantics, ensuring that the optimization of the visual network remains unaffected by normal semantics learning. Then, we divided the words in the report into four parts: normal/abnormal sentences and normal/abnormal semantics, optimizing the network with distinct weights for each partition. The two semantic components, along with visual information, are seamlessly integrated to facilitate the generation of precise and coherent reports. This approach mitigates the impact of noisy normal semantics and reports. Moreover, we develop a novel encoder for abnormal pattern memory, which improves the network’s ability to detect anomalies by capturing and embedding the abnormal patterns of images in the visual encoder. This approach demonstrates excellent performance on the benchmark MIMIC-CXR, surpassing the current state-of-the-art methods.
mmT5: Modular Multilingual Pre-Training Solves Source Language Hallucinations
Authors: Jonas Pfeiffer; Francesco Piccinno; Massimo Nicosia; Xinyi Wang; Machel Reid; Sebastian Ruder
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Multilingual sequence-to-sequence models perform poorly with increased language coverage and fail to consistently generate text in the correct target language in few-shot settings. To address these challenges, we propose mmT5, a modular multilingual sequence-to-sequence model. mmT5 utilizes language-specific modules during pre-training, which disentangle language-specific information from language-agnostic information. We identify representation drift during fine-tuning as a key limitation of modular generative models and develop strategies that enable effective zero-shot transfer. Our model outperforms mT5 at the same parameter sizes by a large margin on representative natural language understanding and generation tasks in 40+ languages. Compared to mT5, mmT5 raises the rate of generating text in the correct language under zero-shot settings from 7% to 99%, thereby greatly alleviating the source language hallucination problem.
ImageNetVC: Zero- and Few-Shot Visual Commonsense Evaluation on 1000 ImageNet Categories
Authors: Heming Xia; Qingxiu Dong; Lei Li; Jingjing Xu; Tianyu Liu; Ziwei Qin; Zhifang Sui
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Recently, Large Language Models (LLMs) have been serving as general-purpose interfaces, posing a significant demand for comprehensive visual knowledge. However, it remains unclear how well current LLMs and their visually augmented counterparts (VaLMs) can master visual commonsense knowledge. To investigate this, we propose ImageNetVC, a human-annotated dataset specifically designed for zero- and few-shot visual commonsense evaluation across 1,000 ImageNet categories. Utilizing ImageNetVC, we benchmark the fundamental visual commonsense knowledge of both unimodal LLMs and VaLMs. Furthermore, we analyze the factors affecting the visual commonsense knowledge of large-scale models, providing insights into the development of language models enriched with visual commonsense knowledge. Our code and dataset are available at https://github.com/hemingkx/ImageNetVC.
MultiCoNER v2: a Large Multilingual dataset for Fine-grained and Noisy Named Entity Recognition
Authors: Besnik Fetahu; Zhiyu Chen; Sudipta Kar; Oleg Rokhlenko; Shervin Malmasi
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: We present MULTICONER V2, a dataset for fine-grained Named Entity Recognition covering 33 entity classes across 12 languages, in both monolingual and multilingual settings. This dataset aims to tackle the following practical challenges in NER: (i) effective handling of fine-grained classes that include complex entities like movie titles, and (ii) performance degradation due to noise generated from typing mistakes or OCR errors. The dataset is compiled from open resources like Wikipedia and Wikidata, and is publicly available. Evaluation based on the XLM-RoBERTa baseline highlights the unique challenges posed by MULTICONER V2: (i) the fine-grained taxonomy is challenging, where the scores are low with macro-F1=0.63 (across all languages), and (ii) the corruption strategy significantly impairs performance, with entity corruption resulting in 9% lower performance relative to non-entity corruptions across all languages. This highlights the greater impact of entity noise in contrast to context noise.
A Query-Parallel Machine Reading Comprehension Framework for Low-resource NER
Authors: Yuhao Zhang; Yongliang Wang
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Named entity recognition (NER) is a fundamental task in natural language processing. Recently, NER has been formulated as a machine reading comprehension (MRC) task, in which manually-crafted queries are used to extract entities of different types. However, current MRC-based NER techniques are limited to extracting a single type of entities at a time and are largely geared towards resource-rich settings. This renders them inefficient during the inference phase, while also leaving their potential untapped for utilization in low-resource settings. We suggest a query-parallel MRC-based approach to address these issues, which is capable of extracting multiple entity types concurrently and is applicable to both resource-rich and resource-limited settings. Specifically, we propose a query-parallel encoder which uses a query-segmented attention mechanism to isolate the semantics of queries and model the query-context interaction with a unidirectional flow. This allows for easier generalization to new entity types or transfer to new domains. After obtaining the query and context representations through the encoder, they are fed into a query-conditioned biaffine predictor to extract multiple entities at once. The model is trained with parameter-efficient tuning technique, making it more data-efficient. We conduct extensive experiments and demonstrate that our model performs competitively against strong baseline methods in resource-rich settings, and achieves state-of-the-art results in low-resource settings, including training-from-scratch, in-domain transfer and cross-domain transfer tasks.
BiSPN: Generating Entity Set and Relation Set Coherently in One Pass
Authors: Yuxin He; Buzhou Tang
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: By modeling the interaction among instances and avoiding error propagation, Set Prediction Networks (SPNs) achieve state-of-the-art performance on the tasks of named entity recognition and relation triple extraction respectively. However, how to jointly extract entities and relation triples via SPNs remains an unexplored problem, where the main challenge is the maintenance of coherence between the predicted entity/relation sets during one-pass generation. In this work, we present Bipartite Set Prediction Network (BiSPN), a novel joint entity-relation extraction model that can efficiently generate entity set and relation set in parallel. To overcome the challenge of coherence, BiSPN is equipped with a novel bipartite consistency loss as well as an entity-relation linking loss during training. Experiments on three biomedical/clinical datasets and a general-domain dataset show that BiSPN achieves new state of the art in knowledge-intensive scene and performs competitively in general-domain, while being more efficient than two-stage joint extraction methods.
MEEP: Is this Engaging? Prompting Large Language Models for Dialogue Evaluation in Multilingual Settings
Authors: Amila Ferron; Amber Shore; Ekata Mitra; Ameeta Agrawal
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: As dialogue systems become more popular, evaluation of their response quality gains importance. Engagingness highly correlates with overall quality and creates a sense of connection that gives human participants a more fulfilling experience. Although qualities like coherence and fluency are readily measured with well-worn automatic metrics, evaluating engagingness often relies on human assessment, which is a costly and time-consuming process. Existing automatic engagingness metrics evaluate the response without the conversation history, are designed for one dataset, or have limited correlation with human annotations. Furthermore, they have been tested exclusively on English conversations. Given that dialogue systems are increasingly available in languages beyond English, multilingual evaluation capabilities are essential. We propose that large language models (LLMs) may be used for evaluation of engagingness in dialogue through prompting, and ask how prompt constructs and translated prompts compare in a multilingual setting. We provide a prompt-design taxonomy for engagingness and find that using selected prompt elements with LLMs, including our comprehensive definition of engagingness, outperforms state-of-the-art methods on evaluation of engagingness in dialogue across multiple languages.
Exploring the Impact of Corpus Diversity on Financial Pretrained Language Models
Authors: Jaeyoung Choe; Keonwoong Noh; Nayeon Kim; Seyun Ahn; Woohwan Jung
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Over the past few years, various domain-specific pretrained language models (PLMs) have been proposed and have outperformed general-domain PLMs in specialized areas such as biomedical, scientific, and clinical domains. In addition, financial PLMs have been studied because of the high economic impact of financial data analysis. However, we found that financial PLMs were not pretrained on sufficiently diverse financial data. This lack of diverse training data leads to a subpar generalization performance, resulting in general-purpose PLMs, including BERT, often outperforming financial PLMs on many downstream tasks. To address this issue, we collected a broad range of financial corpus and trained the Financial Language Model (FiLM) on these diverse datasets. Our experimental results confirm that FiLM outperforms not only existing financial PLMs but also general domain PLMs. Furthermore, we provide empirical evidence that this improvement can be achieved even for unseen corpus groups.
LLMDet: A Third Party Large Language Models Generated Text Detection Tool
Authors: Kangxi Wu; Liang Pang; Huawei Shen; Xueqi Cheng; Tat-Seng Chua
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Generated texts from large language models (LLMs) are remarkably close to high-quality human-authored text, raising concerns about their potential misuse in spreading false information and academic misconduct. Consequently, there is an urgent need for a highly practical detection tool capable of accurately identifying the source of a given text. However, existing detection tools typically rely on access to LLMs and can only differentiate between machine-generated and human-authored text, failing to meet the requirements of fine-grained tracing, intermediary judgment, and rapid detection. Therefore, we propose LLMDet, a model-specific, secure, efficient, and extendable detection tool, that can source text from specific LLMs, such as GPT-2, OPT, LLaMA, and others. In LLMDet, we record the next-token probabilities of salient n-grams as features to calculate proxy perplexity for each LLM. By jointly analyzing the proxy perplexities of LLMs, we can determine the source of the generated text. Experimental results show that LLMDet yields impressive detection performance while ensuring speed and security, achieving 98.54% precision and about × 5.0 faster for recognizing human-authored text. Additionally, LLMDet can effortlessly extend its detection capabilities to a new open-source model. We will provide an open-source tool at https://github.com/TrustedLLM/LLMDet.
RECAP: Towards Precise Radiology Report Generation via Dynamic Disease Progression Reasoning
Authors: Wenjun Hou; Yi Cheng; Kaishuai Xu; Wenjie Li; Jiang Liu
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Automating radiology report generation can significantly alleviate radiologists’ workloads. Previous research has primarily focused on realizing highly concise observations while neglecting the precise attributes that determine the severity of diseases (e.g., small pleural effusion). Since incorrect attributes will lead to imprecise radiology reports, strengthening the generation process with precise attribute modeling becomes necessary. Additionally, the temporal information contained in the historical records, which is crucial in evaluating a patient’s current condition (e.g., heart size is unchanged), has also been largely disregarded. To address these issues, we propose RECAP, which generates precise and accurate radiology reports via dynamic disease progression reasoning. Specifically, RECAP first predicts the observations and progressions (i.e., spatiotemporal information) given two consecutive radiographs. It then combines the historical records, spatiotemporal information, and radiographs for report generation, where a disease progression graph and dynamic progression reasoning mechanism are devised to accurately select the attributes of each observation and progression. Extensive experiments on two publicly available datasets demonstrate the effectiveness of our model.
Causal Intervention for Abstractive Related Work Generation
Authors: Jiachang Liu; Qi Zhang; Chongyang Shi; Usman Naseem; Shoujin Wang; Liang Hu; Ivor Tsang
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Abstractive related work generation has attracted increasing attention in generating coherent related work that helps readers grasp the current research. However, most existing models ignore the inherent causality during related work generation, leading to spurious correlations which downgrade the models’ generation quality and generalizability. In this study, we argue that causal intervention can address such limitations and improve the quality and coherence of generated related work. To this end, we propose a novel Causal Intervention Module for Related Work Generation (CaM) to effectively capture causalities in the generation process. Specifically, we first model the relations among the sentence order, document (reference) correlations, and transitional content in related work generation using a causal graph. Then, to implement causal interventions and mitigate the negative impact of spurious correlations, we use do-calculus to derive ordinary conditional probabilities and identify causal effects through CaM. Finally, we subtly fuse CaM with Transformer to obtain an end-to-end related work generation framework. Extensive experiments on two real-world datasets show that CaM can effectively promote the model to learn causal relations and thus produce related work of higher quality and coherence.
G-SPEED: General SParse Efficient Editing MoDel
Authors: Haoke Zhang; Yue Wang; Juntao Li; Xiabing Zhou; Min Zhang
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Large Language Models (LLMs) have demonstrated incredible capabilities in understanding, generating, and manipulating languages. Through human-model interactions, LLMs can automatically understand human-issued instructions and output the expected contents, which can significantly increase working efficiency. In various types of real-world demands, editing-oriented tasks account for a considerable proportion, which involves an interactive process that entails the continuous refinement of existing texts to meet specific criteria. Due to the need for multi-round human-model interaction and the generation of complicated editing tasks, there is an emergent need for efficient general editing models. In this paper, we propose General SParse Efficient Editing MoDel (G-SPEED), which can fulfill diverse editing requirements through a single model while maintaining low computational costs. Specifically, we first propose a novel unsupervised text editing data clustering algorithm to deal with the data scarcity problem. Subsequently, we introduce a sparse editing model architecture to mitigate the inherently limited learning capabilities of small language models. The experimental outcomes indicate that G-SPEED, with its 508M parameters, can surpass LLMs equipped with 175B parameters. Our code and model checkpoints are available at https://github.com/Banner-Z/G-SPEED.
Attack Prompt Generation for Red Teaming and Defending Large Language Models
Authors: Boyi Deng; Wenjie Wang; Fuli Feng; Yang Deng; Qifan Wang; Xiangnan He
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Large language models (LLMs) are susceptible to red teaming attacks, which can induce LLMs to generate harmful content. Previous research constructs attack prompts via manual or automatic methods, which have their own limitations on construction cost and quality. To address these issues, we propose an integrated approach that combines manual and automatic methods to economically generate high-quality attack prompts. Specifically, considering the impressive capabilities of newly emerged LLMs, we propose an attack framework to instruct LLMs to mimic human-generated prompts through in-context learning. Furthermore, we propose a defense framework that fine-tunes victim LLMs through iterative interactions with the attack framework to enhance their safety against red teaming attacks. Extensive experiments on different LLMs validate the effectiveness of our proposed attack and defense frameworks. Additionally, we release a series of attack prompts datasets named SAP with varying sizes, facilitating the safety evaluation and enhancement of more LLMs.