GenAI4ELab/papercli-papers huggingface dataset:
Play Guessing Game with LLM: Indirect Jailbreak Attack with Implicit Clues
Authors: Zhiyuan Chang; Mingyang Li; Yi Liu; Junjie Wang; Qing Wang; Yang Liu
Venue: ACL 2024 (Findings of the Association for Computational Linguistics: ACL 2024)

Abstract: With the development of LLMs, the security threats of LLMs are getting more and more attention. Numerous jailbreak attacks have been proposed to assess the security defense of LLMs. Current jailbreak attacks primarily utilize scenario camouflage techniques. However their explicitly mention of malicious intent will be easily recognized and defended by LLMs. In this paper, we propose an indirect jailbreak attack approach, Puzzler, which can bypass the LLM’s defensive strategies and obtain malicious response by implicitly providing LLMs with some clues about the original malicious query. In addition, inspired by the wisdom of “When unable to attack, defend” from Sun Tzu’s Art of War, we adopt a defensive stance to gather clues about the original malicious query through LLMs. The experimental results indicate that the Query Success Rate of the Puzzler is 14.0%-82.7% higher than baselines on the most prominent LLMs. Furthermore, when tested against the state-of-the-art jailbreak detection approaches, Puzzler proves to be more effective at evading detection compared to baselines.
Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes
Authors: Sunjun Kweon; Junu Kim; Jiyoun Kim; Sujeong Im; Eunbyeol Cho; Seongsu Bae; Jungwoo Oh; Gyubok Lee; Jong Hak Moon; Seng Chan You; Seungjin Baek; Chang Hoon Han; Yoon Bin Jung; Yohan Jo; Edward Choi
Venue: ACL 2024 (Findings of the Association for Computational Linguistics: ACL 2024)

Abstract: The development of large language models tailored for handling patients’ clinical notes is often hindered by the limited accessibility and usability of these notes due to strict privacy regulations. To address these challenges, we first create synthetic large-scale clinical notes using publicly available case reports extracted from biomedical literature. We then use these synthetic notes to train our specialized clinical large language model, Asclepius. While Asclepius is trained on synthetic data, we assess its potential performance in real-world applications by evaluating it using real clinical notes. We benchmark Asclepius against several other large language models, including GPT-3.5-turbo and other open-source alternatives. To further validate our approach using synthetic notes, we also compare Asclepius with its variants trained on real clinical notes. Our findings convincingly demonstrate that synthetic clinical notes can serve as viable substitutes for real ones when constructing high-performing clinical language models. This conclusion is supported by detailed evaluations conducted by both GPT-4 and medical professionals. All resources—including weights, codes, and data—used in the development of Asclepius will be made publicly accessible for future research.
Extending Context Window of Large Language Models via Semantic Compression
Authors: Weizhi Fei; Xueyan Niu; Pingyi Zhou; Lu Hou; Bo Bai; Lei Deng; Wei Han
Venue: ACL 2024 (Findings of the Association for Computational Linguistics: ACL 2024)

Abstract: Transformer based Large Language Models (LLMs) often impose limitations on the length of the text input to ensure the generation of fluent and relevant responses due to the quadratic complexity. These constraints restrict their applicability in long text scenarios. In this paper, we propose a novel semantic compression method that enables generalization to texts that are 6-8 times longer without incurring significant computational costs or requiring fine-tuning. Our proposed framework draws inspiration from source coding in information theory and employs a pre-trained model to reduce the semantic redundancy of long inputs before passing them to the LLMs for downstream tasks. Experimental results demonstrate that our method effectively extends the context window of LLMs across a range of tasks including question answering, summarization, few-shot learning, and information retrieval. Furthermore, the proposed semantic compression method exhibits consistent fluency in text generation while reducing the associated computational overhead.
Plausible Extractive Rationalization through Semi-Supervised Entailment Signal
Authors: Yeo Wei Jie; Ranjan Satapathy; Erik Cambria
Venue: ACL 2024 (Findings of the Association for Computational Linguistics: ACL 2024)

Abstract: The increasing use of complex and opaque black box models requires the adoption of interpretable measures, one such option is extractive rationalizing models, which serve as a more interpretable alternative. These models, also known as Explain-Then-Predict models, employ an explainer model to extract rationales and subsequently condition the predictor with the extracted information. Their primary objective is to provide precise and faithful explanations, represented by the extracted rationales. In this paper, we take a semi-supervised approach to optimize for the plausibility of extracted rationales. We adopt a pre-trained natural language inference (NLI) model and further fine-tune it on a small set of supervised rationales (10%). The NLI predictor is leveraged as a source of supervisory signals to the explainer via entailment alignment. We show that, by enforcing the alignment agreement between the explanation and answer in a question-answering task, the performance can be improved without access to ground truth labels. We evaluate our approach on the ERASER dataset and show that our approach achieves comparable results with supervised extractive models and outperforms unsupervised approaches by > 100%.
Translation Deserves Better: Analyzing Translation Artifacts in Cross-lingual Visual Question Answering
Authors: ChaeHun Park; Koanho Lee; Hyesu Lim; Jaeseok Kim; Junmo Park; Yu-Jung Heo; Du-Seong Chang; Jaegul Choo
Venue: ACL 2024 (Findings of the Association for Computational Linguistics: ACL 2024)

Abstract: Building a reliable visual question answering (VQA) system across different languages is a challenging problem, primarily due to the lack of abundant samples for training. To address this challenge, recent studies have employed machine translation systems for the cross-lingual VQA task. This involves translating the evaluation samples into a source language (usually English) and using monolingual models (i.e., translate-test). However, our analysis reveals that translated texts contain unique characteristics distinct from human-written ones, referred to as translation artifacts. We find that these artifacts can significantly affect the models, confirmed by extensive experiments across diverse models, languages, and translation processes. In light of this, we present a simple data augmentation strategy that can alleviate the adverse impacts of translation artifacts.
Scented-EAE: Stage-Customized Entity Type Embedding for Event Argument Extraction
Authors: Yu Yang; Jinyu Guo; Kai Shuang; Chenrui Mao
Venue: ACL 2024 (Findings of the Association for Computational Linguistics: ACL 2024)

Abstract: Existing methods for incorporating entities into EAE rely on prompts or NER. They typically fail to explicitly explore the role of entity types, which results in shallow argument comprehension and often encounter three issues: (1) weak semantic associations due to missing role-entity correspondence cues; (2) compromised semantic integrity from abandoning context after recognizing entities regardless of their types; (3) one-sided semantic understanding relying solely on argument role semantics. To tackle these issues, we propose Scented-EAE, an EAE model with stage-customized entity type embedding to explicitly underscore and explore the role of entity types, thus intervening in argument selection. Specifically, at the input stage, we strengthen semantic associations by prompting role-entity correspondence after extending a non-autoregressive decoder as part of the encoder. At the intermediate stage, we preserve semantic integrity by optimizing our proposed BIO-aware NER and EAE via a novel IPE joint learning. At the output stage, we expand semantic understanding dimensions by determining arguments using span selectors from argument roles and entity types. Experiments show that our model achieves state-of-the-art performance on mainstream benchmarks. In addition, it also exhibits robustness in low-resource settings with the help of prompts and entity types.
Fast Randomized Low-Rank Adaptation of Pre-trained Language Models with PAC Regularization
Authors: Zijian Lei; Dong Qian; William Cheung
Venue: ACL 2024 (Findings of the Association for Computational Linguistics: ACL 2024)

Abstract: Low-rank adaptation (LoRA) achieves parameter efficient fine-tuning for large language models (LLMs) by decomposing the model weight update into a pair of low-rank projection matrices. Yet, the memory overhead restricts it to scale up when the model size increases. We propose Randomized LoRA (RLoRA) which adopts Randomized Walsh-Hadamard Transform to achieve significant reduction in the size of trainable parameters compared to LoRA. At the same time, it allows a PAC-Bayes regularizer to be efficiently incorporated to improve generalization. We evaluate the effectiveness of RLoRA on LLMs RoBERTa, GPT-2 and LLaMA-7B using GLUE, E2E and math reasoning benchmarks. With a much lower memory requirement, RLoRA can give similar performance as the SOTA low-rank adaptation methods for these three tasks and significantly better performance under few-shot settings.
SDA: Semantic Discrepancy Alignment for Text-conditioned Image Retrieval
Authors: Yuchen Yang; Yu Wang; Yanfeng Wang
Venue: ACL 2024 (Findings of the Association for Computational Linguistics: ACL 2024)

Abstract: In the realm of text-conditioned image retrieval, models utilize a query composed of a reference image and modification text to retrieve corresponding images. Despite its significance, this task is fraught with challenges, including small-scale datasets due to labeling costs and the complexity of attributes in modification texts. These challenges often result in models learning a generalized representation of the query, thereby missing the semantic correlations of image and text attributes.In this paper, we introduce a general boosting framework designed to address these issues by employing semantic discrepancy alignment. Our framework first leverages the ChatGPT to augment text data by modifying the original modification text’s attributes. The augmented text is then combined with the original reference image to create an augmented composed query. Then we generate corresponding images using GPT-4 for the augmented composed query.We realize the cross-modal semantic discrepancy alignment by formulating distance consistency and neighbor consistency between the image and text domains. Through this novel approach, attribute in the text domain can be more effectively transferred to the image domain, enhancing retrieval performance. Extensive experiments on three prominent datasets validate the effectiveness of our approach, with state-of-the-art results on a majority of evaluation metrics compared to various baseline methods.
Se2: Sequential Example Selection for In-Context Learning
Authors: Haoyu Liu; Jianfeng Liu; Shaohan Huang; Yuefeng Zhan; Hao Sun; Weiwei Deng; Furu Wei; Qi Zhang
Venue: ACL 2024 (Findings of the Association for Computational Linguistics: ACL 2024)

Abstract: The remarkable capability of large language models(LLMs) for in-context learning(ICL) needs to be activated by demonstration examples. Prior work has extensively explored the selection of examples for ICL, predominantly following the “select then organize” paradigm, such approaches often neglect the internal relationships between examples and exist an inconsistency between the training and inference. In this paper, we formulate the problem as a Sequential Selection problem and introduce Se2, a sequential-aware method that leverages the LLM’s feedback on varying context, aiding in capturing inter-relationships and sequential information among examples, significantly enriching the contextuality and relevance of ICL prompts. Meanwhile, we utilize beam search to seek and construct example sequences, enhancing both quality and diversity. Extensive experiments across 23 NLP tasks from 8 distinct categories illustrate that Se2 markedly surpasses competitive baselines and achieves 42% relative improvement over random selection. Further in-depth analysis shows the effectiveness of proposed strategies, highlighting Se2‘s exceptional stability and adaptability across various scenarios. Code available at https://github.com/microsoft/LMOps.
Generation Meets Verification: Accelerating Large Language Model Inference with Smart Parallel Auto-Correct Decoding
Authors: Hanling Yi; Feng Lin; Hongbin Li; Ning Peiyang; Xiaotian Yu; Rong Xiao
Venue: ACL 2024 (Findings of the Association for Computational Linguistics: ACL 2024)

Abstract: This research aims to accelerate the inference speed of large language models (LLMs) with billions of parameters. We propose Smart Parallel Auto-Correct dEcoding (SPACE), an approach designed for achieving lossless acceleration of LLMs. By integrating semi-autoregressive inference and speculative decoding capabilities, SPACE uniquely enables autoregressive LLMs to parallelize token generation and verification. This is realized through a specialized semi-autoregressive supervised fine-tuning process that equips existing LLMs with the ability to simultaneously predict multiple tokens. Additionally, an auto-correct decoding algorithm facilitates the simultaneous generation and verification of token sequences within a single model invocation. Through extensive experiments on a range of LLMs, SPACE has demonstrated inference speedup ranging from 2.7x-4.0x on HumanEval-X while maintaining output quality.
StructEval: Deepen and Broaden Large Language Model Assessment via Structured Evaluation
Authors: Boxi Cao; Mengjie Ren; Hongyu Lin; Xianpei Han; Feng Zhang; Junfeng Zhan; Le Sun
Venue: ACL 2024 (Findings of the Association for Computational Linguistics: ACL 2024)

Abstract: Evaluation is the baton for the development of large language models. Current evaluations typically employ a single-item assessment paradigm for each atomic test objective, which struggle to discern whether a model genuinely possesses the required capabilities or merely memorizes/guesses the answers to specific questions. To this end, this paper proposes a novel evaluation framework referred to as StructEval. Starting from an atomic test objective, StructEval deepens and broadens the evaluation by conducting a structured assessment across multiple cognitive levels and critical concepts, and therefore offers a comprehensive, robust and consistent evaluations for large language models. Experiments on three widely-used benchmarks demonstrate that StructEval serves as a reliable tool for resisting the risk of data contamination, and reducing the interference of potential biases, thereby providing a more reliable and consistent conclusion regarding model capabilities. Our framework also sheds light on the design of future principled and trustworthy LLM evaluation protocols.
Mitigating Privacy Seesaw in Large Language Models: Augmented Privacy Neuron Editing via Activation Patching
Authors: Xinwei Wu; Weilong Dong; Shaoyang Xu; Deyi Xiong
Venue: ACL 2024 (Findings of the Association for Computational Linguistics: ACL 2024)

Abstract: Protecting privacy leakage in large language models remains a paramount challenge. In this paper, we reveal Privacy Seesaw in LLM privacy safeguarding, a phenomenon where measures to secure specific private information inadvertently heighten exposure risks for other privacy. Through comprehensive analysis, we identify the amount of targeted privacy data and the volume of edited privacy neurons as the two central triggers to this issue. To mitigate privacy seesaw, we propose Augmented Privacy Neuron Editing via Activation Patching (APNEAP), a novel framework designed to well balance model performance with privacy protection. The proposed APNEAP augments collected private data by automatically synthesizing new private data, which deactivates the first trigger to the privacy seesaw issue. Additionally, it adapts activation patching to privacy neuron editing for switching off the second trigger to the privacy seesaw problem. Experimental results show that the proposed APNEAP is capable of alleviating the privacy seesaw phenomenon and offers a more stable and reliable approach to privacy protection in LLMs than previous methods.
Which Information Matters? Dissecting Human-written Multi-document Summaries with Partial Information Decomposition
Authors: Laura Mascarell; Yan LHomme; Majed El Helou
Venue: ACL 2024 (Findings of the Association for Computational Linguistics: ACL 2024)

Abstract: Understanding the nature of high-quality summaries is crucial to further improve the performance of multi-document summarization. We propose an approach to characterize human-written summaries using partial information decomposition, which decomposes the mutual information provided by all source documents into union, redundancy, synergy, and unique information. Our empirical analysis on different MDS datasets shows that there is a direct dependency between the number of sources and their contribution to the summary.
BadActs: A Universal Backdoor Defense in the Activation Space
Authors: Biao Yi; Sishuo Chen; Yiming Li; Tong Li; Baolei Zhang; Zheli Liu
Venue: ACL 2024 (Findings of the Association for Computational Linguistics: ACL 2024)

Abstract: Backdoor attacks pose an increasingly severe security threat to Deep Neural Networks (DNNs) during their development stage. In response, backdoor sample purification has emerged as a promising defense mechanism, aiming to eliminate backdoor triggers while preserving the integrity of the clean content in the samples. However, existing approaches have been predominantly focused on the word space, which are ineffective against feature-space triggers and significantly impair performance on clean data. To address this, we introduce a universal backdoor defense that purifies backdoor samples in the activation space by drawing abnormal activations towards optimized minimum clean activation distribution intervals. The advantages of our approach are twofold: (1) By operating in the activation space, our method captures from surface-level information like words to higher-level semantic concepts such as syntax, thus counteracting diverse triggers; (2) the fine-grained continuous nature of the activation space allows for more precise preservation of clean content while removing triggers. Furthermore, we propose a detection module based on statistical information of abnormal activations, to achieve a better trade-off between clean accuracy and defending performance. Extensive experiments on diverse datasets and against diverse attacks (including syntax and style attacks) demonstrate that our defense achieves state-of-the-art performance.
ReactXT: Understanding Molecular “Reaction-ship” via Reaction-Contextualized Molecule-Text Pretraining
Authors: Zhiyuan Liu; Yaorui Shi; An Zhang; Sihang Li; Enzhi Zhang; Xiang Wang; Kenji Kawaguchi; Tat-Seng Chua
Venue: ACL 2024 (Findings of the Association for Computational Linguistics: ACL 2024)

Abstract: Molecule-text modeling, which aims to facilitate molecule-relevant tasks with a textual interface and textual knowledge, is an emerging research direction. Beyond single molecules, studying reaction-text modeling holds promise for helping the synthesis of new materials and drugs. However, previous works mostly neglect reaction-text modeling: they primarily focus on modeling individual molecule-text pairs or learning chemical reactions without texts in context. Additionally, one key task of reaction-text modeling – experimental procedure prediction – is less explored due to the absence of an open-source dataset. The task is to predict step-by-step actions of conducting chemical experiments and is crucial to automating chemical synthesis. To resolve the challenges above, we propose a new pretraining method, ReactXT, for reaction-text modeling, and a new dataset, OpenExp, for experimental procedure prediction. Specifically, ReactXT features three types of input contexts to incrementally pretrain LMs. Each of the three input contexts corresponds to a pretraining task to improve the text-based understanding of either reactions or single molecules. ReactXT demonstrates consistent improvements in experimental procedure prediction and molecule captioning and offers competitive results in retrosynthesis. Our code is available at https://github.com/syr-cn/ReactXT.
Multi-modal Concept Alignment Pre-training for Generative Medical Visual Question Answering
Authors: Quan Yan; Junwen Duan; Jianxin Wang
Venue: ACL 2024 (Findings of the Association for Computational Linguistics: ACL 2024)

Abstract: Medical Visual Question Answering (Med-VQA) seeks to accurately respond to queries regarding medical images, a task particularly challenging for open-ended questions. This study unveils the Multi-modal Concept Alignment Pre-training (MMCAP) approach for generative Med-VQA, leveraging a knowledge graph sourced from medical image-caption datasets and the Unified Medical Language System. MMCAP advances the fusion of visual and textual medical knowledge via a graph attention network and a transformer decoder. Additionally, it incorporates a Type Conditional Prompt in the fine-tuning phase, markedly boosting the accuracy and relevance of answers to open-ended questions. Our tests on benchmark datasets illustrate MMCAP’s superiority over existing methods, demonstrating its high efficiency in data-limited settings and effective knowledge-image alignment capability.
Exploring Ordinality in Text Classification: A Comparative Study of Explicit and Implicit Techniques
Authors: Siva Rajesh Kasa; Aniket Goel; Karan Gupta; Sumegh Roychowdhury; Pattisapu Priyatam; Anish Bhanushali; Prasanna Srinivasa Murthy
Venue: ACL 2024 (Findings of the Association for Computational Linguistics: ACL 2024)

Abstract: Ordinal Classification (OC) is a widely encountered challenge in Natural Language Processing (NLP), with applications in various domains such as sentiment analysis, rating prediction, and more. Previous approaches to tackle OC have primarily focused on modifying existing or creating novel loss functions that explicitly account for the ordinal nature of labels. However, with the advent of Pre-trained Language Models (PLMs), it became possible to tackle ordinality through the implicit semantics of the labels as well. This paper provides a comprehensive theoretical and empirical examination of both these approaches. Furthermore, we also offer strategic recommendations regarding the most effective approach to adopt based on specific settings.