36 accepted papers
We propose Contextual History for Adaptive and Simple Exploitation (CHASE), a novel multi-turn method for Large Language Model (LLM) jailbreaking. Rather than directly attack an LLM that may be difficult to jailbreak, CHASE first collects jailbroken histories from an easy-to-jailbreak LLM and then t
Large Language Models (LLMs) are reshaping the design and capabilities of intelligent tutoring systems (ITS) by providing powerful generative, reasoning and interaction abilities, which surpass traditional rule-based approaches. This survey presents a structured overview of LLM-based ITS and analyze
Dual-system theory distinguishes between fast, intuitive System 1 and slow, deliberative System 2. While this dichotomy describes many forms of reasoning, it oversimplifies the reality of expert legal reasoning. Legal reasoning is not merely a process of slow, logical deliberation. It is intrinsical
While virtually all existing work on Automated Essay Scoring (AES) models an essay as a word sequence, we put forward the novel view that an essay can be modeled as a graph and subsequently propose GAT-AES, a graph-attention network approach to AES. GAT-AES models the interactions among essay traits
We introduce InternLM-Law, a large language model (LLM) tailored for addressing diverse legal tasks related to Chinese laws. These tasks range from responding to standard legal questions (e.g., legal exercises in textbooks) to analyzing complex real-world legal situations. Our work contributes to Ch…
Legal Judgment Prediction (LJP) seeks to predict case outcomes given available case information, offering practical value for both legal professionals and laypersons. However, a key limitation of existing LJP models is their limited adaptability to statutory revisions. Current SOTA models are neithe…
Meme captioning, the task of generating a sentence that describes the meaning of a meme, is both challenging and important in advancing Computational Meme Understanding (CMU). However, existing research has not explored its decomposition into subtasks or its connections to other CMU tasks. To addres
Automated meme understanding requires systems to demonstrate fine-grained visual recognition, commonsense reasoning, and extensive cultural knowledge. However, existing benchmarks for meme understanding only concern narrow aspects of meme semantics. To fill this gap, we present MemeQA, a dataset of…
Multimodal neural machine translation (MNMT) has received increasing attention due to its widespread applications in various fields such as cross-border e-commerce and cross-border social media platforms. The task aims to integrate other modalities, such as the visual modality, with textual data to
While AI systems are capable of reading texts and seeing images, they typically perceive surface information explicitly conveyed with limited abilities to comprehend hidden messages (e.g., a double-edged remark). We propose the novel task of advertisement understanding: given an advertisement, which…
While steady progress has been made on the task of automated essay scoring (AES) in the past decade, much of the recent work in this area has focused on developing models that beat existing models on a standard evaluation dataset. While improving performance numbers remains an important goal in the…
Automated essay scoring (AES), the task of automatically assigning a score to an essay that summarizes its quality, is a challenging task that remains largely unsolved despite more than 50 years of research. This survey paper discusses the milestones in AES research and reflects on future directions…
Legal Judgment Prediction (LJP) has attracted significant attention in recent years. However, previous studies have primarily focused on cases involving only a single defendant, skipping multi-defendant cases due to complexity and difficulty. To advance research, we introduce CMDL, a large-scale rea…
Computational Meme Understanding, which concerns the automated comprehension of memes, has garnered interest over the last four years and is facing both substantial opportunities and challenges. We survey this emerging area of research by first introducing a comprehensive taxonomy for memes along th…
Cross-prompt automated essay scoring (AES), an under-investigated but challenging task that has gained increasing popularity in the AES community, aims to train an AES system that can generalize well to prompts that are unseen during model training. While recently-developed cross-prompt AES models h…
The majority of the recently developed models for automated essay scoring (AES) are evaluated solely on the ASAP corpus. However, ASAP is not without its limitations. For instance, it is not clear whether models trained on ASAP can generalize well when evaluated on other corpora. In light of these l…
Legal Judgment Prediction (LJP) refers to the task of automatically predicting judgment results (e.g., charges, law articles and term of penalty) given the fact description of cases. While SOTA models have achieved high accuracy and F1 scores on public datasets, existing datasets fail to evaluate sp…
We present LawBench, the first evaluation benchmark composed of 20 tasks aimed to assess the ability of Large Language Models (LLMs) to perform Chinese legal-related tasks. LawBench is meticulously crafted to enable precise assessment of LLMs’ legal capabilities from three cognitive levels that corr…
Recent years have seen increasing attention on Legal Case Retrieval (LCR), a key task in the area of Legal AI that concerns the retrieval of cases from a large legal database of historical cases that are similar to a given query. This paper presents a survey of the major milestones made in LCR resea…
The aim of the Universal Anaphora initiative is to push forward the state of the art in anaphora and anaphora resolution by expanding the aspects of anaphoric interpretation which are or can be reliably annotated in anaphoric corpora, producing unified standards to annotate and encode these annotati…
Propaganda campaigns have long been used to influence public opinion via disseminating biased and/or misleading information. Despite the increasing prevalence of propaganda content on the Internet, few attempts have been made by AI researchers to analyze such content. We introduce the task of multim…
We present PairSpanBERT, a SpanBERT-based pre-trained model specialized for bridging resolution. To this end, we design a novel pre-training objective that aims to learn the contexts in which two mentions are implicitly linked to each other from a large amount of data automatically generated either…
We examine the extent to which supervised bridging resolvers can be improved without employing additional labeled bridging data by proposing a novel constrained multi-task learning framework for bridging resolution, within which we (1) design cross-task consistency constraints to guide the learning…
Recent years have seen the successful application of deep learning to software engineering (SE). In particular, the development and use of pre-trained models of source code has enabled state-of-the-art results to be achieved on a wide variety of SE tasks. This paper provides an overview of this rapi…
We introduce a new task in hyperbole processing, deexaggeration, which concerns the recovery of the meaning of what is being exaggerated in a hyperbolic sentence in the form of a structured representation. In this paper, we lay the groundwork for the computational study of understanding hyperbole by…
We present DiscoSense, a benchmark for commonsense reasoning via understanding a wide variety of discourse connectives. We generate compelling distractors in DiscoSense using Conditional Adversarial Filtering, an extension of Adversarial Filtering that employs conditional generation. We show that st…
The state of bridging resolution research is rather unsatisfactory: not only are state-of-the-art resolvers evaluated in unrealistic settings, but the neural models underlying these resolvers are weaker than those used for entity coreference resolution. In light of these problems, we evaluate bridgi…
We adapt Lee et al.’s (2018) span-based entity coreference model to the task of end-to-end discourse deixis resolution in dialogue, specifically by proposing extensions to their model that exploit task-specific characteristics. The resulting model, dd-utt, achieves state-of-the-art results on the fo…
While significant progress has been made on the task of Legal Judgment Prediction (LJP) in recent years, the incorrect predictions made by SOTA LJP models can be attributed in part to their failure to (1) locate the key event information that determines the judgment, and (2) exploit the cross-task c…
Automatic legal judgment prediction (LJP) has recently received increasing attention in the natural language processing community in part because of its practical values as well as the associated research challenges. We present an overview of the major milestones made in LJP research covering multip…
While Yu and Poesio (2020) have recently demonstrated the superiority of their neural multi-task learning (MTL) model to rule-based approaches for bridging anaphora resolution, there is little understanding of (1) how it is better than the rule-based approaches (e.g., are the two approaches making s…
We propose a neural event coreference model in which event coreference is jointly trained with five tasks: trigger detection, entity coreference, anaphoricity determination, realis detection, and argument extraction. To guide the learning of this complex model, we incorporate cross-task consistency…
Despite recent promising results on the application of span-based models for event reference interpretation, there is a lack of understanding of what has been improved. We present an empirical analysis of a state-of-the-art span-based event reference systems with the goal of providing the general NL…
User targeting is an essential task in the modern advertising industry: given a package of ads for a particular category of products (e.g., green tea), identify the online users to whom the ad package should be targeted. A (ad package specific) user targeting model is typically trained using histori…
Motivated by the recent successful application of span-based models to entity-based information extraction tasks, we investigate span-based models for event coreference resolution, focusing on determining (1) whether the successes of span-based models of entity coreference can be extended to event c…
Bridging reference resolution is an anaphora resolution task that is arguably more challenging and less studied than entity coreference resolution. Given that significant progress has been made on coreference resolution in recent years, we believe that bridging resolution will receive increasing att…