ymoslem/acl-abstracts huggingface dataset:
HALLUCANA: Fixing LLM Hallucination with A Canary Lookahead (2025)

In this paper, we present HALLUCANA, a canary lookahead to detect and correct factual hallucinations of Large Language Models (LLMs) in long-form generation. HALLUCANA detects and intervenes as soon as traces of hallucination emerge, during and even before generation. To support timely detection, we exploit the internal factuality representation in the LLM hidden space, where we investigate various proxies to the LLMs' factuality self-assessment, and discuss its relation to the models' context familiarity from their pre-training. On biography generation, our method improves generation quality by up to 2.5x, while consuming over 6 times less compute.
Enhancing Adversarial Transferability in Visual-Language Pre-training Models via Local Shuffle and Sample-based Attack (2025)

Visual-Language Pre-training (VLP) models have achieved significant performance across various downstream tasks. However, they remain vulnerable to adversarial examples. While prior efforts focus on improving the adversarial transferability of multimodal adversarial examples through cross-modal interactions, these approaches suffer from overfitting issues, due to a lack of input diversity by relying excessively on information from adversarial examples in one modality when crafting attacks in another. To address this issue, we draw inspiration from strategies in some adversarial training methods and propose a novel attack called Local Shuffle and Sample-based Attack (LSSA). LSSA randomly shuffles one of the local image blocks, thus expanding the original image-text pairs, generating adversarial images, and sampling around them. Then, it utilizes both the original and sampled images to generate the adversarial texts. Extensive experiments on multiple models and datasets demonstrate that LSSA significantly enhances the transferability of multimodal adversarial examples across diverse VLP models and downstream tasks. Moreover, LSSA outperforms other advanced attacks on Large Vision-Language Models.
Dis2Dis: Explaining Ambiguity in Fact-Checking (2025)

Ambiguity is a linguistic tool for encoding information efficiently, yet it also causes misunderstandings and disagreements. It is particularly relevant to the domain of misinformation, as fact-checking ambiguous claims is difficult even for experts. In this paper we argue that instead of predicting a veracity label for which there is genuine disagreement, it would be more beneficial to explain the ambiguity. Thus, this work introduces claim disambiguation, a constrained generation task, for explaining ambiguous claims in fact-checking. This involves editing them to spell out an interpretation that can then be unequivocally supported by the given evidence. We collect a dataset of 1501 such claim revisions and conduct experiments with sequence-to-sequence models. The performance is compared to a simple copy baseline and a Large Language Model baseline. The best results are achieved by employing Minimum Bayes Decoding, with a BertScore F1 of 92.22. According to human evaluation, the model successfully disambiguates the claims 72{\%} of the time.
Enhancing Visual-Language Modality Alignment in Large Vision Language Models via Self-Improvement (2025)

Large vision-language models (LVLMs) have achieved impressive results in visual question-answering and reasoning tasks through vision instruction tuning on specific datasets. However, there remains significant room for improvement in aligning visual and language modalities. Existing methods often depend on external models or data, leading to uncontrollable and unstable alignment results. In this paper, we propose SIMA, a self-improvement framework that enhances visual and language modality alignment without external dependencies. SIMA leverages existing vision instruction tuning datasets to self-generate responses, incorporating an in-context self-critic mechanism that constructs preference pairs for tuning. Crucially, our approach allows LVLMs to act as critics by designing effective critic prompts, eliminating the need for additional fine-tuning with external instruction data. We introduce three novel visual metrics within the self-critic process to guide judgement, significantly improving the accuracy of self-critic. Through extensive experiments across 14 hallucination and comprehensive benchmarks, we demonstrate that SIMA significantly improves LVLM`s performance and outperforms previous approaches, achieving superior modality alignment.
RePD: Defending Jailbreak Attack through a Retrieval-based Prompt Decomposition Process (2025)

In this study, we introduce RePD, an innovative attack Retrieval-based Prompt Decomposition framework designed to mitigate the risk of jailbreak attacks on large language models (LLMs). Despite rigorous pre-training and fine-tuning focused on ethical alignment, LLMs are still susceptible to jailbreak exploits. RePD operates on a one-shot learning model, wherein it accesses a database of pre-collected jailbreak prompt templates to identify and decompose harmful inquiries embedded within user prompts. This process involves integrating the decomposition of the jailbreak prompt into the user`s original query into a one-shot learning example to effectively teach the LLM to discern and separate malicious components. Consequently, the LLM is equipped to first neutralize any potentially harmful elements before addressing the user`s prompt in a manner that aligns with its ethical guidelines. RePD is versatile and compatible with a variety of open-source LLMs acting as agents. Through comprehensive experimentation with both harmful and benign prompts, we have demonstrated the efficacy of our proposed RePD in enhancing the resilience of LLMs against jailbreak attacks, without compromising their performance in responding to typical user requests.
ChatCRS: Incorporating External Knowledge and Goal Guidance for LLM-based Conversational Recommender Systems (2025)

This paper aims to efficiently enable large language models (LLMs) to use external knowledge and goal guidance in conversational recommender system (CRS) tasks. Advanced LLMs (e.g., ChatGPT) are limited in domain-specific CRS tasks for 1) generating grounded responses with recommendation-oriented knowledge, or 2) proactively leading the conversations through different dialogue goals. In this work, we first analyze those limitations through a comprehensive evaluation, showing the necessity of external knowledge and goal guidance which contribute significantly to the recommendation accuracy and language quality. In light of this finding, we propose a novel ChatCRS framework to decompose the complex CRS task into several sub-tasks through the implementation of 1) a knowledge retrieval agent using a tool-augmented approach to reason over external Knowledge Bases and 2) a goal-planning agent for dialogue goal prediction. Experimental results on two multi-goal CRS datasets reveal that ChatCRS sets new state-of-the-art benchmarks, improving language quality of informativeness by 17{\%} and proactivity by 27{\%}, and achieving a tenfold enhancement in recommendation accuracy.
Data-Efficiently Learn Large Language Model for Universal 3D Scene Perception (2025)

3D scene understanding has gained significant attention due to its wide range of applications. However, existing methods for 3D scene understanding are limited to specific downstream tasks, which hinders their practicality in real-world applications. This paper presents Chat-3D, which combines the 3D visual perceptual ability of pre-trained 3D representations and the impressive reasoning and conversation capabilities of advanced LLMs to achieve the first universal dialogue systems for 3D scenes. Specifically, we align 3D representations into the feature space of LLMs, thus enabling LLMs to perceive the 3D world. Given the scarcity of 3D scene-text data, we propose a three-stage training strategy to efficiently utilize the available data for better alignment. To enhance the reasoning ability and develop a user-friendly interaction scheme, we further construct a high-quality object-centric 3D instruction dataset and design an associated object-centric prompt. With limited data, Chat-3D achieves a 82.2{\%} relative score compared with GPT-4 on the constructed instruction dataset, and comparable performance to state-of-the-art LLM-based methods.
UnifiedMLLM: Enabling Unified Representation for Multi-modal Multi-tasks With Large Language Model (2025)

Significant advancements has recently been achieved in the field of multi-modal large language models (MLLMs), demonstrating their remarkable capabilities in understanding and reasoning across diverse tasks. However, these models are often trained for specific tasks and rely on task-specific input-output formats, limiting their applicability to a broader range of tasks. This raises a fundamental question: Can we develop a unified approach to represent and handle different multi-modal tasks to maximize the generalizability of MLLMs? In this paper, we propose UnifiedMLLM, a comprehensive model designed to represent various tasks using a unified representation. Our model exhibits strong capabilities in comprehending the implicit intent of user instructions and preforming reasoning. In addition to generating textual responses, our model also outputs task tokens and grounding tokens, serving as indicators of task types and task granularity. These outputs are subsequently routed through the task router and directed to specific expert models for task completion. To train our model, we construct a task-specific dataset and an 100k multi-task dataset encompassing complex scenarios. Employing a three-stage training strategy, we equip our model with robust reasoning and task processing capabilities while preserving its generalization capacity and knowledge reservoir. Extensive experiments showcase the impressive performance of our unified representation approach across various tasks, surpassing existing methodologies. Furthermore, our approach exhibits exceptional scalability and generality.
PEMV: Improving Spatial Distribution for Emotion Recognition in Conversations Using Proximal Emotion Mean Vectors (2025)

Emotion Recognition in Conversation (ERC) aims to identify the emotions expressed in each utterance within a dialogue. Existing research primarily focuses on the analysis of contextual structure in dialogue and the interactions between different emotions. Nonetheless, ERC datasets often contain difficult-to-classify samples and suffer from imbalanced label distributions, which pose challenges to the spatial distribution of dialogue features. To tackle this issue, we propose a method that generates Proximal Emotion Mean Vectors (PEMV) based on emotion feature queues to optimize the spatial representation of text features. We design a Center Loss based on PEMVs to pull hard-to-classify samples closer to their respective category centers and employ Angle Loss to maximize the angular separation between different PEMVs. Furthermore, we utilize PEMV as a classifier to better adapt to the spatial structure of dialogue features. Extensive experiments on three widely used benchmark datasets demonstrate that our method achieves state-of-the-art performance and validates its effectiveness in optimizing feature space representations.
DiscoverGPT: Multi-task Fine-tuning Large Language Model for Related Table Discovery (2025)

Natural language understanding over tabular data has played a significant role in data discovery tasks such as joinable and unionable table search. State-of-the-art approaches adopt large language models (LLMs) pre-trained over massive text corpora to learn and evaluate the table semantic relatedness. Existing methods typically follow a pretrain-and-finetune paradigm, namely fine-tuning an LLM using tabular data with table relatedness labels. To enhance model`s understanding of tabular data, recent studies include auxiliary tasks such as entity resolution and column type classification in the fine-tuning phase. In spite of achieving performance gain from these supervisions, there is a lack of study on how these supervisions complement or even contrast each other, leading to a subpar performance on the final data discovery tasks. In this paper, we propose a simple yet effective multi-task fine-tuning framework named DiscoverGPT that holistically discovers and leverages the intricate relationships among the supervisions to optimize the performance on the data discovery task. Moreover, DiscoverGPT is plug-and-play that allows a broad range of open-domain auxiliary tasks to be incorporated, by utilizing the generative power of LLMs. We demonstrate the usability and effectiveness of DiscoverGPT with baseline comparisons and ablation studies. DiscoverGPT outperforms the best performing baseline by up to 7{\%} in F1 score.
Can GPT-4 Sway Experts' Investment Decisions? (2025)

In the post-Turing era, evaluating large language models (LLMs) involves assessing generated text based on readers' decisions rather than merely its indistinguishability from human-produced content. This paper explores how LLM-generated text impacts readers' decisions, focusing on both amateur and expert audiences. Our findings indicate that GPT-4 can generate persuasive analyses affecting the decisions of both amateurs and professionals. Furthermore, we evaluate the generated text from the aspects of grammar, convincingness, logical coherence, and usefulness. The results highlight a high correlation between real-world evaluation through audience decisions and the current multi-dimensional evaluators commonly used for generative models. Overall, this paper shows the potential and risk of using generated text to sway human decisions and also points out a new direction for evaluating generated text, i.e., leveraging the decisions of readers. We release our dataset to assist future research.
PolyJoin: Semantic Multi-key Joinable Table Search in Data Lakes (2025)

Given a query table, how can we effectively discover multi-key joinable tables on the web? This can be seen as a retrieval task, where users can lookup on the web for tables related to an existing one. Searching and discovering such joinable tables is critical to data analysts and data scientists for reporting, establishing correlations and training machine learning models. Existing joinable table search methods have mostly focused on single key (unary) joins, where a single column is the join key. However, these methods are ineffective when dealing with join keys composed of multiple columns (n-ary joins), which are prevalent on web table corpora. In this paper, we introduce PolyJoin, which finds multi-key semantically-joinable tables on the web, given a query table. PolyJoin employs a multi-key encoder and a novel self-supervised training method to generate the representations of multiple join keys, preserving the alignment across multiple columns. In particular, PolyJoin is equipped with a hierarchical contrastive learning technique to further enhance the model`s semantic understanding of multi-key joinable tables. PolyJoin outperforms the state-of-the-art methods by 2.89{\%} and 3.67{\%} with respect to MAP@30 and R@30 on two real-world web table benchmarks, respectively.
Marrying LLMs with Dynamic Forecasting: A Graph Mixture-of-expert Perspective (2025)

Dynamical system modeling is a crucial area of research in machine learning with extensive applications in physics and social science. Recent data-driven approaches often employ graph neural networks (GNNs) to learn relationships in dynamical systems using message passing mechanisms. Despite their advancements, these methods often suffer from performance degradation when it comes to potential environmental change with distribution shifts in real-world applications. In this work, we propose a new perspective which leverages large language models (LLMs) to enhance the generalization capabilities of dynamical system modeling. In particular, we develop a novel framework named LLM Judge with Graph Mixture-of-expert LEGO which incorporates multiple graph experts to learn diverse dynamics within the systems. More importantly, LEGO utilizes LLMs with hierarchical prompts at object, edge, and system levels as a context-aware routing function to determine which experts carry the most relevant information to different environments. The whole framework is optimized by updating the weights and expert parameters in an alternative fashion. Extensive experiments across various datasets demonstrate the effectiveness of our proposed LEGO in comparison to extensive baselines.
DialogGen: Multi-modal Interactive Dialogue System with Multi-turn Text-Image Generation (2025)

Text-to-image (T2I) generation models have significantly advanced in recent years. However, effective interaction with these models is challenging for average users due to the need for specialized prompt engineering knowledge and the inability to perform multi-turn image generation, hindering a dynamic and iterative creation process. Recent attempts have tried to equip Multi-modal Large Language Models (MLLMs) with T2I models to bring the user`s natural language instructions into reality. Hence, the output modality of MLLMs is extended, and the multi-turn generation quality of T2I models is enhanced thanks to the strong multi-modal comprehension ability of MLLMs. However, many of these works face challenges in identifying correct output modalities and generating coherent images accordingly as the number of output modalities increases and the conversations go deeper. Therefore, we propose DialogGen, an effective pipeline to align off-the-shelf MLLMs and T2I models to build a Multi-modal Interactive Dialogue System (MIDS) for multi-turn Text-to-Image generation. It is composed of drawing prompt alignment, careful training data curation, and error correction. Moreover, as the field of MIDS flourishes, comprehensive benchmarks are urgently needed to evaluate MIDS fairly in terms of output modality correctness and multi-modal output coherence. To address this issue, we introduce the Multi-modal Dialogue Benchmark (DialogBen), a comprehensive bilingual benchmark designed to assess the ability of MLLMs to generate accurate and coherent multi-modal content that supports image editing. It contains two evaluation metrics to measure the model`s ability to switch modalities and the coherence of the output images. Our extensive experiments on DialogBen and user study demonstrate the effectiveness of DialogGen in producing correct output modalities and coherent multi-modal outputs compared with other State-of-the-Art models. We hope that DialogBen can contribute to the community for building more powerful MIDS.
RELexED: Retrieval-Enhanced Legal Summarization with Exemplar Diversity (2025)

This paper addresses the task of legal summarization, which involves distilling complex legal documents into concise, coherent summaries. Current approaches often struggle with content theme deviation and inconsistent writing styles due to their reliance solely on source documents. We propose RELexED, a retrieval-augmented framework that utilizes exemplar summaries along with the source document to guide the model. RELexED employs a two-stage exemplar selection strategy, leveraging a determinantal point process to balance the trade-off between similarity of exemplars to the query and diversity among exemplars, with scores computed via influence functions. Experimental results on two legal summarization datasets demonstrate that RELexED significantly outperforms models that do not utilize exemplars and those that rely solely on similarity-based exemplar selection.
CLaMP 2: Multimodal Music Information Retrieval Across 101 Languages Using Large Language Models (2025)

Challenges in managing linguistic diversity and integrating various musical modalities are faced by current music information retrieval systems. These limitations reduce their effectiveness in a global, multimodal music environment. To address these issues, we introduce CLaMP 2, a system compatible with 101 languages that supports both ABC notation (a text-based musical notation format) and MIDI (Musical Instrument Digital Interface) for music information retrieval. CLaMP 2, pre-trained on 1.5 million ABC-MIDI-text triplets, includes a multilingual text encoder and a multimodal music encoder aligned via contrastive learning. By leveraging large language models, we obtain refined and consistent multilingual descriptions at scale, significantly reducing textual noise and balancing language distribution. Our experiments show that CLaMP 2 achieves state-of-the-art results in both multilingual semantic search and music classification across modalities, thus establishing a new standard for inclusive and global music information retrieval.
LogRules: Enhancing Log Analysis Capability of Large Language Models through Rules (2025)

Currently, large language models (LLMs) have achieved impressive performance in natural language processing tasks. However, LLMs still exhibit many hallucinations when analyzing system logs, which is due to the implicit knowledge and rules in logs that LLMs cannot capture. Based on this, we propose LogRules, a lightweight log analysis framework that generates and utilizes rules through LLMs. LogRules consists of three stages: an induction stage, an alignment stage, and a reasoning stage. Firstly, in the induction stage, an strong LLM (e.g., GPT-4o-mini) is tasked with generating a series of rules related to logs, which are then validated on the training set. When the rules are confirmed to produce correct reasoning results, they are added to a rule repository. Secondly, considering that the LLMs with small size ($\approx$8B parameters) still face challenges in utilizing rules, we design an alignment method based on rule-case contrastive preference optimization (CPO) to effectively enhance the rule reasoning capabilities of these LLMs. Finally, in the reasoning stage, the LLM constructs prompt using the rule repository and performs log analysis on the test set. Experiments show that LogRules outperforms LLM-based methods in log parsing and anomaly detection tasks, and achieves better performance compared to case-based methods.
Audio Description Generation in the Era of LLMs and VLMs: A Review of Transferable Generative AI Technologies (2025)

Audio descriptions (ADs) function as acoustic commentaries designed to assist blind persons and persons with visual impairments in accessing digital media content on television and in movies, among other settings. As an accessibility service typically provided by trained AD professionals, the generation of ADs demands significant human effort, making the process both time-consuming and costly. Recent advancements in natural language processing (NLP) and computer vision (CV), particularly in large language models (LLMs) and vision-language models (VLMs), have allowed for getting a step closer to automatic AD generation. This paper reviews the technologies pertinent to AD generation in the era of LLMs and VLMs: we discuss how state-of-the-art NLP and CV technologies can be applied to generate ADs and identify essential research directions for the future.