GenAI4ELab/papercli-papers huggingface dataset:
Adversarial Preference Optimization: Enhancing Your Alignment via RM-LLM Game
Authors: Pengyu Cheng; Yifan Yang; Jian Li; Yong Dai; Tianhao Hu; Peixin Cao; Nan Du; Xiaolong Li
Venue: ACL 2024 (Findings of the Association for Computational Linguistics: ACL 2024)

Abstract: Human preference alignment is essential to improve the interaction quality of large language models (LLMs). Existing alignment methods depend on manually annotated preference data to guide the LLM optimization directions. However, continuously updating LLMs for alignment raises a distribution gap between model-generated samples and human-annotated responses, hindering training effectiveness. To mitigate this issue, previous methods require additional preference annotation on newly generated samples to adapt to the shifted distribution, which consumes a large amount of annotation resources. Targeting more efficient human preference optimization, we propose an Adversarial Preference Optimization (APO) framework, in which the LLM and the reward model update alternatively via a min-max game. Through adversarial training, the reward model can adapt to the shifted generation distribution of the LLM without any additional annotation. With comprehensive experiments, we find the proposed adversarial training framework further enhances existing alignment baselines in terms of LLM helpfulness and harmlessness. The code is at https://github.com/Linear95/APO.
Pinpointing Diffusion Grid Noise to Enhance Aspect Sentiment Quad Prediction
Authors: Linan Zhu; Xiangfan Chen; Xiaolei Guo; Chenwei Zhang; Zhechao Zhu; Zehai Zhou; Xiangjie Kong
Venue: ACL 2024 (Findings of the Association for Computational Linguistics: ACL 2024)

Abstract: Aspect sentiment quad prediction (ASQP) has garnered significant attention in aspect-based sentiment analysis (ABSA). Current ASQP research primarily relies on pre-trained generative language models to produce templated sequences, often complemented by grid-based auxiliary methods. Despite these efforts, the persistent challenge of generation instability remains unresolved and the effectiveness of grid methods remains underexplored in current studies. To this end, we introduce Grid Noise Diffusion Pinpoint Network (GDP), a T5-based generative model aiming to tackle the issue of generation instability. The model consists of three novel modules, including Diffusion Vague Learning (DVL) to facilitate effective model learning and enhance overall robustness; Consistency Likelihood Learning (CLL) to discern the characteristics and commonalities of sentiment elements and thus reduce the impact of distributed noise; and GDP-FOR, a novel generation template, to enable models to generate outputs in a more natural way. Extensive experiments on four datasets demonstrate the remarkable effectiveness of our approach in addressing ASQP tasks.
Continual Contrastive Spoken Language Understanding
Authors: Umberto Cappellazzo; Enrico Fini; Muqiao Yang; Daniele Falavigna; Alessio Brutti; Bhiksha Raj
Venue: ACL 2024 (Findings of the Association for Computational Linguistics: ACL 2024)

Abstract: Recently, neural networks have shown impressive progress across diverse fields, with speech processing being no exception. However, recent breakthroughs in this area require extensive offline training using large datasets and tremendous computing resources. Unfortunately, these models struggle to retain their previously acquired knowledge when learning new tasks continually. In this paper, we investigate the problem of learning sequence-to-sequence models for spoken language understanding in a class-incremental learning (CIL) setting and we propose COCONUT, a CIL method that relies on the combination of experience replay and contrastive learning. Through a modified version of the standard supervised contrastive loss, COCONUT preserves the learned representations by pulling closer samples from the same class and pushing away the others. Moreover, we leverage a multimodal contrastive loss that helps the model learn more discriminative representations of the new data by aligning audio and text features. We also investigate different contrastive designs to combine the strengths of the contrastive loss with teacher-student architectures used for distillation. Experiments on two established SLU datasets reveal the effectiveness of our proposed approach and significant improvements over the baselines. We also show that COCONUT can be combined with methods that operate on the decoder side of the model, resulting in further metrics improvements.
LLM as Prompter: Low-resource Inductive Reasoning on Arbitrary Knowledge Graphs
Authors: Kai Wang; Yuwei Xu; Zhiyong Wu; Siqiang Luo
Venue: ACL 2024 (Findings of the Association for Computational Linguistics: ACL 2024)

Abstract: Knowledge Graph (KG) inductive reasoning, which aims to infer missing facts from new KGs that are not seen during training, has been widely adopted in various applications. One critical challenge of KG inductive reasoning is handling low-resource scenarios with scarcity in both textual and structural aspects. In this paper, we attempt to address this challenge with Large Language Models (LLMs). Particularly, we utilize the state-of-the-art LLMs to generate a graph-structural prompt to enhance the pre-trained Graph Neural Networks (GNNs), which brings us new methodological insights into the KG inductive reasoning methods, as well as high generalizability in practice. On the methodological side, we introduce a novel pretraining and prompting framework ProLINK, designed for low-resource inductive reasoning across arbitrary KGs without requiring additional training. On the practical side, we experimentally evaluate our approach on 36 low-resource KG datasets and find that ProLINK outperforms previous methods in three-shot, one-shot, and zero-shot reasoning tasks, exhibiting average performance improvements by 20%, 45%, and 147%, respectively. Furthermore, ProLINK demonstrates strong robustness for various LLM promptings as well as full-shot scenarios.
Unsupervised Parsing by Searching for Frequent Word Sequences among Sentences with Equivalent Predicate-Argument Structures
Authors: Junjie Chen; Xiangheng He; Danushka Bollegala; Yusuke Miyao
Venue: ACL 2024 (Findings of the Association for Computational Linguistics: ACL 2024)

Abstract: Unsupervised constituency parsing focuses on identifying word sequences that form a syntactic unit (i.e., constituents) in target sentences. Linguists identify the constituent by evaluating a set of Predicate-Argument Structure (PAS) equivalent sentences where we find the constituent appears more frequently than non-constituents (i.e., the constituent corresponds to a frequent word sequence within the sentence set). However, such frequency information is unavailable in previous parsing methods that identify the constituent by observing sentences with diverse PAS. In this study, we empirically show that constituents correspond to frequent word sequences in the PAS-equivalent sentence set. We propose a frequency-based parser, span-overlap, that (1) computes the span-overlap score as the word sequence’s frequency in the PAS-equivalent sentence set and (2) identifies the constituent structure by finding a constituent tree with the maximum span-overlap score. The parser achieves state-of-the-art level parsing accuracy, outperforming existing unsupervised parsers in eight out of ten languages. Additionally, we discover a multilingual phenomenon: participant-denoting constituents tend to have higher span-overlap scores than equal-length event-denoting constituents, meaning that the former tend to appear more frequently in the PAS-equivalent sentence set than the latter. The phenomenon indicates a statistical difference between the two constituent types, laying the foundation for future labeled unsupervised parsing research.
Data-Centric Explainable Debiasing for Improving Fairness in Pre-trained Language Models
Authors: Yingji Li; Mengnan Du; Rui Song; Xin Wang; Ying Wang
Venue: ACL 2024 (Findings of the Association for Computational Linguistics: ACL 2024)

Abstract: Human-like social bias of pre-trained language models (PLMs) on downstream tasks have attracted increasing attention. The potential flaws in the training data are the main factor that causes unfairness in PLMs. Existing data-centric debiasing strategies mainly leverage explicit bias words (defined as sensitive attribute words specific to demographic groups) for counterfactual data augmentation to balance the training data. However, they lack consideration of implicit bias words potentially associated with explicit bias words in complex distribution data, which indirectly harms the fairness of PLMs. To this end, we propose a **Data**-Centric **Debias**ing method (named Data-Debias), which uses an explainability method to search for implicit bias words to assist in debiasing PLMs. Specifically, we compute the feature attributions of all tokens using the Integrated Gradients method, and then treat the tokens that have a large impact on the model’s decision as implicit bias words. To make the search results more precise, we iteratively train a biased model to amplify the bias with each iteration. Finally, we use the implicit bias words searched in the last iteration to assist in debiasing PLMs. Extensive experimental results on multiple PLMs debiasing on three different classification tasks demonstrate that Data-Debias achieves state-of-the-art debiasing performance and strong generalization while maintaining predictive abilities.
Knowledge-Driven Cross-Document Relation Extraction
Authors: Monika Jain; Raghava Mutharaju; Kuldeep Singh; Ramakanth Kavuluru
Venue: ACL 2024 (Findings of the Association for Computational Linguistics: ACL 2024)

Abstract: Relation extraction (RE) is a well-known NLP application often treated as a sentence or document-level task. However, a handful of recent efforts explore it across documents or in the cross-document setting (CrossDocRE). This is distinct from the single document case because different documents often focus on disparate themes, while text within a document tends to have a single goal.Current CrossDocRE efforts do not consider domain knowledge, which are often assumed to be known to the reader when documents are authored. Here, we propose a novel approach, KXDocRE, that embed domain knowledge of entities with input text for cross-document RE. Our proposed framework has three main benefits over baselines: 1) it incorporates domain knowledge of entities along with documents’ text; 2) it offers interpretability by producing explanatory text for predicted relations between entities 3) it improves performance over the prior methods. Code and models are available at https://github.com/kracr/cross-doc-relation-extraction.
Injecting Salesperson’s Dialogue Strategies in Large Language Models with Chain-of-Thought Reasoning
Authors: Wen Chang; Yun-Nung Chen
Venue: ACL 2024 (Findings of the Association for Computational Linguistics: ACL 2024)

Abstract: Recent research in dialogue systems focuses on two main categories: task-oriented (TOD) and open-domain (chit-chat) dialogues. TOD systems help users complete specific tasks, while open-domain systems aim to create engaging conversations. However, user intents often emerge during interactions. A recent study introduced SalesBot, simulating dialogues that transition from chit-chat to task-oriented scenarios to train sales agents. Unfortunately, the initial data lacked smooth transitions and coherent long dialogues, resulting in unnatural interactions. This paper presents SalesBot 2.0, an improved dataset leveraging commonsense knowledge from large language models (LLMs) through strategic prompting. Additionally, we introduce SalesAgent, a novel model trained on salesperson interactions using chain-of-thought (CoT) reasoning. This model excels in transitioning topics, understanding user intents, and selecting appropriate strategies.Experiments with diverse user simulations validate our method’s effectiveness in controlling dialogue strategies in LLMs. SalesBot 2.0 enhances coherence and reduces aggression, improving model learning for sales-customer interactions.
KG-Adapter: Enabling Knowledge Graph Integration in Large Language Models through Parameter-Efficient Fine-Tuning
Authors: Shiyu Tian; Yangyang Luo; Tianze Xu; Caixia Yuan; Huixing Jiang; Chen Wei; Xiaojie Wang
Venue: ACL 2024 (Findings of the Association for Computational Linguistics: ACL 2024)

Abstract: Although large language models (LLMs) show remarkable capabilities and generalizability across various tasks, they are criticized for lack of expertise. One promising solution is to combine knowledge graphs (KGs) with LLMs, and recent studies focus on integrating KGs into LLMs through prompt-based methods. However, these approaches fail to use the structural information of the KGs, suffer from the problem of knowledge conflict, and over-reliance on super LLMs. To address these challenges, we propose KG-Adapter, a parameter-level KG integration method based on parameter-efficient fine-tuning (PEFT). Specifically, we introduce a novel adapter structure designed for decoder-only LLMs, which can encode KGs from both node-centered and relation-centered perspectives, and then perform joint reasoning with LLMs to generate responses end-to-end. Experiments with diverse models on four datasets for two different tasks all demonstrate significant improvements. With only 28M parameters trained, we make the 7B-parameter LLM outperform the previous full-parameter fine-tuned state-of-the-art method and comparable to the prompt-based ChatGPT methods.
Just Ask One More Time! Self-Agreement Improves Reasoning of Language Models in (Almost) All Scenarios
Authors: Lei Lin; Jiayi Fu; Pengli Liu; Qingyang Li; Yan Gong; Junchen Wan; Fuzheng Zhang; Zhongyuan Wang; Di Zhang; Kun Gai
Venue: ACL 2024 (Findings of the Association for Computational Linguistics: ACL 2024)

Abstract: Although chain-of-thought (CoT) prompting combined with language models has achieved encouraging results on complex reasoning tasks, the naive greedy decoding used in CoT prompting usually causes the repetitiveness and local optimality. To address this shortcoming, ensemble-optimization tries to obtain multiple reasoning paths to get the final answer assembly. However, current ensemble-optimization methods either simply employ rule-based post-processing such as self-consistency, or train an additional model based on several task-related human annotations to select the best one among multiple reasoning paths, yet fail to generalize to realistic settings where the type of input questions is unknown or the answer format of reasoning paths is unknown. To avoid their limitations, we propose Self-Agreement, a generalizable ensemble-optimization method applying in almost all scenarios where the type of input questions and the answer format of reasoning paths may be known or unknown. Self-agreement firstly samples from language model’s decoder to generate a diverse set of reasoning paths, and subsequently prompts the language model one more time to determine the optimal answer by selecting the most agreed answer among the sampled reasoning paths. Self-agreement simultaneously achieves remarkable performance on six public reasoning benchmarks and superior generalization capabilities.
Evaluating LLMs’ Mathematical Reasoning in Financial Document Question Answering
Authors: Pragya Srivastava; Manuj Malik; Vivek Gupta; Tanuja Ganu; Dan Roth
Venue: ACL 2024 (Findings of the Association for Computational Linguistics: ACL 2024)

Abstract: Large Language Models (LLMs), excel in natural language understanding, but their capability for complex mathematical reasoning with a hybrid of structured tables and unstructured text remain uncertain. This study explores LLMs’ mathematical reasoning on four financial tabular question-answering datasets: TATQA, FinQA, ConvFinQA, and Multihiertt. Through extensive experiments with various models and prompting techniques, we assess how LLMs adapt to complex tables and mathematical tasks. We focus on sensitivity to table complexity and performance variations with an increasing number of arithmetic reasoning steps. The results provide insights into LLMs’ capabilities and limitations in handling complex mathematical scenarios for semi-structured tables. Ultimately, we introduce a novel prompting technique EEDP tailored to semi-structured documents, matching or outperforming baselines performance while providing a nuanced understanding of LLMs abilities.
Improving In-Context Learning with Prediction Feedback for Sentiment Analysis
Authors: Hongling Xu; Qianlong Wang; Yice Zhang; Min Yang; Xi Zeng; Bing Qin; Ruifeng Xu
Venue: ACL 2024 (Findings of the Association for Computational Linguistics: ACL 2024)

Abstract: Large language models (LLMs) have achieved promising results in sentiment analysis through the in-context learning (ICL) paradigm. However, their ability to distinguish subtle sentiments still remains a challenge. Inspired by the human ability to adjust understanding via feedback, this paper enhances ICL by incorporating prior predictions and feedback, aiming to rectify sentiment misinterpretation of LLMs. Specifically, the proposed framework consists of three steps: (1) acquiring prior predictions of LLMs, (2) devising predictive feedback based on correctness, and (3) leveraging a feedback-driven prompt to refine sentiment understanding. Experimental results across nine sentiment analysis datasets demonstrate the superiority of our framework over conventional ICL methods, with an average F1 improvement of 5.95%.
Can Large Language Models Mine Interpretable Financial Factors More Effectively? A Neural-Symbolic Factor Mining Agent Model
Authors: Zhiwei Li; Ran Song; Caihong Sun; Wei Xu; Zhengtao Yu; Ji-Rong Wen
Venue: ACL 2024 (Findings of the Association for Computational Linguistics: ACL 2024)

Abstract: Finding interpretable factors for stock returns is the most vital issue in the empirical asset pricing domain. As data-driven methods, existing factor mining models can be categorized into symbol-based and neural-based models. Symbol-based models are interpretable but inefficient, while neural-based approaches are efficient but lack interpretability. Hence, mining interpretable factors effectively presents a significant challenge. Inspired by the success of Large Language Models (LLMs) in various tasks, we propose a FActor Mining Agent (FAMA) model that enables LLMs to integrate the strengths of both neural and symbolic models for factor mining. In this paper, FAMA consists of two main components: Cross-Sample Selection (CSS) and Chain-of-Experience (CoE). CSS addresses the homogeneity challenges in LLMs during factor mining by assimilating diverse factors as in-context samples, whereas CoE enables LLMs to leverage past successful mining experiences, expediting the mining of effective factors. Experimental evaluations on real-world stock market data demonstrate the effectiveness of our approach by surpassing the SOTA RankIC by 0.006 and RankICIR by 0.105 in predicting S&P 500 returns. Furthermore, the investment simulation shows that our model can achieve superior performance with an annualized return of 38.4% and a Sharpe ratio of 667.2%.
Discerning and Resolving Knowledge Conflicts through Adaptive Decoding with Contextual Information-Entropy Constraint
Authors: Xiaowei Yuan; Zhao Yang; Yequan Wang; Shengping Liu; Jun Zhao; Kang Liu
Venue: ACL 2024 (Findings of the Association for Computational Linguistics: ACL 2024)

Abstract: Large language models (LLMs) internalize enormous parametric knowledge during pre-training. Concurrently, realistic applications necessitate external contextual knowledge to aid models on the underlying tasks. This raises a crucial dilemma known as knowledge conflicts, where the contextual knowledge clashes with the parametric knowledge. However, existing decoding works are specialized in resolving knowledge conflicts and could inadvertently deteriorate performance in absence of conflicts. In this paper, we propose an adaptive decoding method, termed as contextual information-entropy constraint decoding (COIECD), to discern whether the knowledge conflicts occur and resolve them. It can improve the model’s faithfulness to conflicting context, and simultaneously maintain high performance among non-conflicting context. Our experiments show that COIECD exhibits strong performance and robustness over knowledge conflicts in realistic datasets.
SALAD-Bench: A Hierarchical and Comprehensive Safety Benchmark for Large Language Models
Authors: Lijun Li; Bowen Dong; Ruohui Wang; Xuhao Hu; Wangmeng Zuo; Dahua Lin; Yu Qiao; Jing Shao
Venue: ACL 2024 (Findings of the Association for Computational Linguistics: ACL 2024)

Abstract: In the rapidly evolving landscape of Large Language Models (LLMs), ensuring robust safety measures is paramount. To meet this crucial need, we propose SALAD-Bench, a safety benchmark specifically designed for evaluating LLMs, attack, and defense methods. Distinguished by its breadth, SALAD-Bench transcends conventional benchmarks through its large scale, rich diversity, intricate taxonomy spanning three levels, and versatile functionalities.SALAD-Bench is crafted with a meticulous array of questions, from standard queries to complex ones enriched with attack, defense modifications and multiple-choice. To effectively manage the inherent complexity, we introduce an innovative evaluators: the LLM-based MD-Judge for QA pairs with a particular focus on attack-enhanced queries, ensuring a seamless, and reliable evaluation. Above components extend SALAD-Bench from standard LLM safety evaluation to both LLM attack and defense methods evaluation, ensuring the joint-purpose utility. Our extensive experiments shed light on the resilience of LLMs against emerging threats and the efficacy of contemporary defense tactics. Data and evaluator are released under https://github.com/OpenSafetyLab/SALAD-BENCH
Extracting and Encoding: Leveraging Large Language Models and Medical Knowledge to Enhance Radiological Text Representation
Authors: Pablo Messina; Rene Vidal; Denis Parra; Alvaro Soto; Vladimir Araujo
Venue: ACL 2024 (Findings of the Association for Computational Linguistics: ACL 2024)

Abstract: Advancing representation learning in specialized fields like medicine remains challenging due to the scarcity of expert annotations for text and images. To tackle this issue, we present a novel two-stage framework designed to extract high-quality factual statements from free-text radiology reports in order to improve the representations of text encoders and, consequently, their performance on various downstream tasks.In the first stage, we propose a Fact Extractor that leverages large language models (LLMs) to identify factual statements from well-curated domain-specific datasets. In the second stage, we introduce a Fact Encoder (CXRFE) based on a BERT model fine-tuned with objective functions designed to improve its representations using the extracted factual data. Our framework also includes a new embedding-based metric (CXRFEScore) for evaluating chest X-ray text generation systems, leveraging both stages of our approach. Extensive evaluations show that our fact extractor and encoder outperform current state-of-the-art methods in tasks such as sentence ranking, natural language inference, and label extraction from radiology reports. Additionally, our metric proves to be more robust and effective than existing metrics commonly used in the radiology report generation literature. The code of this project is available at https://github.com/PabloMessina/CXR-Fact-Encoder.