ymoslem/acl-abstracts huggingface dataset:
What Makes for Good Visual Instructions? Synthesizing Complex Visual Reasoning Instructions for Visual Instruction Tuning (2025)

Visual instruction tuning is crucial for enhancing the zero-shot generalization capability of Multi-modal Large Language Models (MLLMs). In this paper, we aim to investigate a fundamental question: {\textquotedblleft}what makes for good visual instructions{\textquotedblright}. Through a comprehensive empirical study, we find that instructions focusing on complex visual reasoning tasks are particularly effective in improving the performance of MLLMs, with results correlating to instruction complexity. Based on this insight, we develop a systematic approach to automatically create high-quality complex visual reasoning instructions. Our approach employs a synthesize-complicate-reformulate paradigm, leveraging multiple stages to gradually increase the complexity of the instructions while guaranteeing quality. Based on this approach, we create the ComVint dataset with 32K examples, and fine-tune four MLLMs on it. Experimental results consistently demonstrate the enhanced performance of all compared MLLMs, such as a 27.86{\%} and 27.60{\%} improvement for LLaVA on MME-Perception and MME-Cognition, respectively. Our code and data are publicly available at the link: https://github.com/RUCAIBox/ComVint.
TriFine: A Large-Scale Dataset of Vision-Audio-Subtitle for Tri-Modal Machine Translation and Benchmark with Fine-Grained Annotated Tags (2025)

Current video-guided machine translation (VMT) approaches primarily use coarse-grained visual information, resulting in information redundancy, high computational overhead, and neglect of audio content. Our research demonstrates the significance of fine-grained visual and audio information in VMT from both data and methodological perspectives. From the data perspective, we have developed a large-scale dataset TriFine, the first vision-audio-subtitle tri-modal VMT dataset with annotated multimodal fine-grained tags. Each entry in this dataset not only includes the triples found in traditional VMT datasets but also encompasses seven fine-grained annotation tags derived from visual and audio modalities. From the methodological perspective, we propose a Fine-grained Information-enhanced Approach for Translation (FIAT). Experimental results have shown that, in comparison to traditional coarse-grained methods and text-only models, our fine-grained approach achieves superior performance with lower computational overhead. These findings underscore the pivotal role of fine-grained annotated information in advancing the field of VMT.
Can Many-Shot In-Context Learning Help LLMs as Evaluators? A Preliminary Empirical Study (2025)

Utilizing Large Language Models (LLMs) as evaluators to assess the performance of other LLMs has garnered attention. However, this evaluation approach is affected by potential biases within LLMs, raising concerns about the accuracy and reliability of the evaluation results of LLMs. To address this issue, we propose and explore two many-shot In-Context Learning (ICL) prompt templates to help LLM evaluators mitigate potential biases: Many-Shot with Reference (MSwR) and Many-Shot without Reference (MSoR). Specifically, the former utilizes in-context examples with model-generated rationales as references, while the latter does not include these references. Using these prompt designs, we investigate the impact of increasing the number of in-context examples on the consistency and quality of the evaluation results. Experimental results show that advanced LLMs, such as GPT-4, perform better in the many-shot regime than in the zero-shot regime. Furthermore, in most cases, MSwR performs significantly better than MSoR.
GEAR: A Simple GENERATE, EMBED, AVERAGE AND RANK Approach for Unsupervised Reverse Dictionary (2025)

Reverse Dictionary (RD) is the task of obtaining the most relevant word or set of words given a textual description or dictionary definition. Effective RD methods have applications in accessibility, translation or writing support systems. Moreover, in NLP research we find RD to be used to benchmark text encoders at various granularities, as it often requires word, definition and sentence embeddings. In this paper, we propose a simple approach to RD that leverages LLMs in combination with embedding models. Despite its simplicity, this approach outperforms supervised baselines in well studied RD datasets, while also showing less overfitting. We also conduct a number of experiments on different dictionaries and analyze how different styles, registers and target audiences impact the quality of RD systems. We conclude that, on average, untuned embeddings alone fare way below an LLM-only baseline (although they are competitive in highly technical dictionaries), but are crucial for boosting performance in combined methods.
Momentum Posterior Regularization for Multi-hop Dense Retrieval (2025)

Multi-hop question answering (QA) often requires sequential retrieval (multi-hop retrieval), where each hop retrieves missing knowledge based on information from previous hops. To facilitate more effective retrieval, we aim to distill knowledge from a posterior retrieval, which has access to posterior information like an answer, into a prior retrieval used during inference when such information is unavailable. Unfortunately, current methods for knowledge distillation in one-time retrieval are ineffective for multi-hop QA due to two issues: 1) posterior information is often defined as the response (i.e. answers), which may not clearly connect to the query without intermediate retrieval; and 2) the large knowledge gap between prior and posterior retrievals makes distillation using existing methods unstable, even resulting in performance loss. As such, we propose MoPo (Momentum Posterior Regularization) with two key innovations: 1) Posterior information of one hop is defined as a query-focus summary from the golden knowledge of the previous and current hops; 2) We develop an effective training strategy where the posterior retrieval is updated along with the prior retrieval via momentum moving average method, allowing smoother and effective distillation. Experiments on HotpotQA and StrategyQA demonstrate that MoPo outperforms existing baselines in both retrieval and downstream QA tasks.
CaDRL: Document-level Relation Extraction via Context-aware Differentiable Rule Learning (2025)

Document-level Relation Extraction (DocRE) aims to extract relations from documents. Compared with sentence-level relation extraction, it is necessary to extract long-distance dependencies. Existing methods enhance the output of trained DocRE models either by learning logical rules or by extracting rules from annotated data and then injecting them into the model. However, these approaches can result in suboptimal performance due to incorrect rule set constraints. To mitigate this issue, we propose Context-aware differentiable rule learning or CaDRL for short, a novel differentiable rule-based framework that learns the doc-specific logical rule to avoid generating suboptimal constraints. Specifically, we utilize Transformer-based relation attention to encode document and relation information, thereby learning the contextual information of the relation. We employ a sequence-generated differentiable rule decoder to generate relational probabilistic logic rules at each reasoning step. We also introduce a parameter sharing training mechanism in CaDRL to reconcile the DocRE model and the rule learning module. Extensive experimental results on three DocRE datasets demonstrate that CaDRL outperforms existing rule-based frameworks, significantly improving DocRE performance and making predictions more interpretable and logical.
TEF: Causality-Aware Taxonomy Expansion via Front-Door Criterion (2025)

Taxonomy expansion is a primary method for enriching taxonomies, involving appending a large number of additional nodes (i.e., queries) to an existing taxonomy (i.e., seed), with the crucial step being the identification of the appropriate anchor (parent node) for each query by incorporating the structural information of the seed. Despite advancements, existing research still faces an inherent challenge of spurious query-anchor matching, often due to various interference factors (e.g., the consistency of sibling nodes), resulting in biased identifications. To address the bias in taxonomy expansion caused by unobserved factors, we introduce the Structural Causal Model (SCM), known for its bias elimination capabilities, to prevent these factors from confounding the task through backdoor paths. Specifically, we employ the Front-Door Criterion, which guides the decomposition of the expansion process into a parser module and a connector. This enables the proposed causal-aware Taxonomy Expansion model to isolate confounding effects and reveal the true causal relationship between the query and the anchor. Extensive experiments on three benchmarks validate the effectiveness of TEF, with a notable 6.1{\%} accuracy improvement over the state-of-the-art on the SemEval16-Environment dataset.
Inside-Outside Algorithm for Probabilistic Product-Free Lambek Categorial Grammar (2025)

The inside-outside algorithm is widely utilized in statistical models related to context-free grammars. It plays a key role in the EM estimation of probabilistic context-free grammars. In this work, we introduce an inside-outside algorithm for Probabilistic Lambek Categorical Grammar (PLCG)
Perceive the Passage of Time: A Systematic Evaluation of Large Language Model in Temporal Relativity (2025)

Temporal perception is crucial for Large Language Models(LLMs) to effectively understand the world. However, current benchmarks primarily focus on temporal reasoning, falling short in understanding the temporal characteristics involving temporal perception, particularly in understanding temporal relativity. In this paper, we introduce TempBench, a comprehensive benchmark designed to evaluate the temporal-relative ability of LLMs. TempBench encompasses 4 distinct scenarios: Physiology, Psychology, Cognition and Mixture. We conduct an extensive experiments on GPT-4, a series of Llama and other popular LLMs. The experiment results demonstrate a significant performance gap between LLMs and humans in temporal-relative capability. Furthermore, the error types of temporal-relative ability in LLMs are proposed to thoroughly analyze the impact of multiple aspects and emphasize the associated challenges. We anticipate that TempBench will drive further advancements in enhancing the temporal-perceiving capabilities of L
Hit the Sweet Spot! Span-Level Ensemble for Large Language Models (2025)

Ensembling various LLMs to unlock their complementary potential and leverage their individual strengths is highly valuable. Previous studies typically focus on two main paradigms: sample-level and token-level ensembles. Sample-level ensemble methods either select or blend fully generated outputs, which hinders dynamic correction and enhancement of outputs during the generation process. On the other hand, token-level ensemble methods enable real-time correction through fine-grained ensemble at each generation step. However, the information carried by an individual token is quite limited, leading to suboptimal decisions at each step. To address these issues, we propose \textbf{SweetSpan}, a span-level ensemble method that effectively balances the need for real-time adjustments and the information required for accurate ensemble decisions. Our approach involves two key steps: First, we have each candidate model independently generate candidate spans based on the shared prefix. Second, we calculate perplexity scores to facilitate mutual evaluation among the candidate models and achieve robust span selection by filtering out unfaithful scores. To comprehensively evaluate ensemble methods, we propose a new challenging setting (ensemble models with significant performance gaps) in addition to the standard setting (ensemble the best-performing models) to assess the performance of model ensembles in more realistic scenarios. Experimental results in both standard and challenging settings across various language generation tasks demonstrate the effectiveness, robustness, and versatility of our approach compared with previous ensemble methods.
PToco: Prefix-based Token-level Collaboration Enhances Reasoning for Multi-LLMs (2025)

Collaboration between multiple Large Language Models (LLMs) has attracted significant attention for its potential to mitigate hallucinations and enhance reasoning capabilities. Previous approaches, such as multi-agent debate and decoding-time integration, either rely on highly capable models with strong self-reflection abilities or are limited to models sharing the same tokenizer. To address these limitations, we introduce PToco (Prefix-based Token-level Collaboration), a novel mechanism that enables effective collaboration among less capable LLMs, independent of tokenizer differences. PToco uses a prefix-grouping method to extract consensus among tokens with varying levels of granularity, ensuring coherent and robust token generation across multiple models. Experimental results on a series of reasoning tasks demonstrate that PToco significantly improves performance over individual models. Furthermore, this approach generalizes well across different quantities and sizes of participating models, providing a more flexible and efficient solution for multi-LLM ensembles.
MAGRET: Machine-generated Text Detection with Rewritten Texts (2025)

With the quick advancement in text generation ability of Large Language Mode(LLM), concerns about the misuse of machine-generated content have grown, raising potential violations of legal and ethical standards. Some existing studies concentrate on detecting machine-generated text in open-source models using in-model features, but their performance on closed-source large models is limited. This limitation occurs because, in the closed-source model detection, the only reference that can be obtained is the texts, which may differ significantly due to random sampling. In this paper, we demonstrate that texts generated by the same model can align both semantically and statistically under similar prompts, facilitating effective detection and traceability. Specifically, we fine-tune a BERT encoder through contrastive learning to achieve semantic alignment in randomly generated texts from the same model. Then, we propose a method called \textbf{Ma}chine-\textbf{G}enerated Text Detection with \textbf{Re}written \textbf{T}exts, which designed several prompt refactoring methods and used them to request rewritten text from LLMs. Semantic and statistical relationships between rewritten and original texts provide a basis for detection and traceability. Finally, we expanded the text dataset with multi-parameter random sampling and verified the performance of MAGRET on three text-generated datasets. Experimental results show that previous methods struggle with closed-source model detection, while our approach significantly outperforms baseline methods in this regard. It also shows MagRet`s stable performance in detection and tracing tasks across various randomly sampled texts.
Structured List-Grounded Question Answering (2025)

Document-grounded dialogue systems aim to answer user queries by leveraging external information. Previous studies have mainly focused on handling free-form documents, often overlooking structured data such as lists, which can represent a range of nuanced semantic relations. Motivated by the observation that even advanced language models like GPT-3.5 often miss semantic cues from lists, this paper aims to enhance question answering (QA) systems for better interpretation and use of structured lists. To this end, we introduce the LIST2QA dataset, a novel benchmark to evaluate the ability of QA systems to respond effectively using list information. This dataset is created from unlabeled customer service documents using language models and model-based filtering processes to enhance data quality, and can be used to fine-tune and evaluate QA models. Apart from directly generating responses through fine-tuned models, we further explore the explicit use of Intermediate Steps for Lists (ISL), aligning list items with user backgrounds to better reflect how humans interpret list items before generating responses. Our experimental results demonstrate that models trained on LIST2QA with our ISL approach outperform baselines across various metrics. Specifically, our fine-tuned Flan-T5-XL model shows increases of 3.1{\%} in ROUGE-L, 4.6{\%} in correctness, 4.5{\%} in faithfulness, and 20.6{\%} in completeness compared to models without applying filtering and the proposed ISL method.
Low-Resource Language Expansion and Translation Capacity Enhancement for LLM: A Study on the Uyghur (2025)

Although large language models have significantly advanced natural language generation, their potential in low-resource machine translation has not yet been fully explored, especially for languages that translation models have not been trained on. In this study, we provide a detailed demonstration of how to efficiently expand low-resource languages for large language models and significantly enhance the model`s translation ability, using Uyghur as an example. The process involves four stages: collecting and pre-processing monolingual data, conducting continuous pre-training with extensive monolingual data, fine-tuning with less parallel corpora using translation supervision, and proposing a direct preference optimization based on translation self-evolution (DPOSE) on this basis. Extensive experiments have shown that our strategy effectively expands the low-resource languages supported by large language models and significantly enhances the model`s translation ability in Uyghur with less parallel data. Our research provides detailed insights for expanding other low-resource languages into large language models.
Unraveling the Mystery: Defending Against Jailbreak Attacks Via Unearthing Real Intention (2025)

As Large Language Models (LLMs) become more advanced, the security risks they pose also increase. Ensuring that LLM behavior aligns with human values, particularly in mitigating jailbreak attacks with elusive and implicit intentions, has become a significant challenge. To address this issue, we propose a jailbreak defense method called Real Intentions Defense (RID), which involves two phases: soft extraction and hard deletion. In the soft extraction phase, LLMs are leveraged to extract unbiased, genuine intentions, while in the hard deletion phase, a greedy gradient-based algorithm is used to remove the least important parts of a sentence, based on the insight that words with smaller gradients have less impact on its meaning. We conduct extensive experiments on Vicuna and Llama2 models using eight state-of-the-art jailbreak attacks and six benchmark datasets. Our results show a significant reduction in both Attack Success Rate (ASR) and Harmful Score of jailbreak attacks, while maintaining overall model performance. Further analysis sheds light on the underlying mechanisms of our approach.
A Flash in the Pan: Better Prompting Strategies to Deploy Out-of-the-Box LLMs as Conversational Recommendation Systems (2025)

Conversational Recommendation Systems (CRSs) are a particularly interesting application for out-of-the-box LLMs due to their potential for eliciting user preferences and making recommendations in natural language across a wide set of domains. Somewhat surprisingly, we find however that in such a conversational application, the more questions a user answers about their preferences, the worse the model`s recommendations become. We demonstrate this phenomenon on a previously published dataset as well as two novel datasets which we contribute. We also explain why earlier benchmarks failed to detect this round-over-round performance loss, highlighting the importance of the evaluation strategy we use and expanding upon Li et al. (2023a). We also present preference elicitation and recommendation strategies that mitigate this degradation in performance, beating state-of-the-art results, and show how three underlying models, GPT-3.5, GPT-4, and Claude 3.5 Sonnet, differently impact these strategies. Our datasets and code are available at https://github.com/CtrlVGustavo/A-Flash- in-the-Pan-CRS.
Rule-KBQA: Rule-Guided Reasoning for Complex Knowledge Base Question Answering with Large Language Models (2025)

Knowledge base question answering (KBQA) is recognized as a challenging task, especially when parsing complex questions into executable logical forms. Traditional semantic parsing (SP)-based approaches exhibit inconsistent performance in handling various complex questions. As large language models (LLMs) have exhibited exceptional reasoning ability and language comprehension, recent studies have employed LLMs for semantic parsing to directly generate logical forms that can be executed on knowledge bases (KBs) to achieve the desired results. However, these methods of relying exclusively on LLMs to ensure grammaticality, faithfulness, and controllability may diminish their effectiveness due to hallucinations in the reasoning process. In this paper, we introduce Rule-KBQA, a framework that employs learned rules to guide the generation of logical forms. The proposed method contains two phases, an induction phase and a deduction phase. In the induction phase, we initially extract rules from the existing data and then employ the Rule-Following Fine-Tuned (RFFT) LLM to generate additional rules, ultimately constructing a comprehensive rule library. In the deduction phase, a symbolic agent, guided by learned rules, explores the environment KB to incrementally construct executable logical forms. Meanwhile, we leverage the discriminative capability of LLMs to evaluate the plausibility of candidate decisions. Extensive experiments indicate that our method achieves competitive results on standard KBQA datasets, clearly demonstrating its effectiveness.
Mitigating Language Confusion through Inference-time Intervention (2025)

Although large language models (LLMs) trained on extensive multilingual corpora exhibit impressive language transfer, they often fail to respond in the user`s desired language due to corpus imbalances, an embarrassingly simple problem known as the language confusion. However, existing solutions like in-context learning and supervised fine-tuning (SFT) have drawbacks: in-context learning consumes context window space, diminishing attention as text lengthens, while SFT requires extensive, labor-intensive data collection. To overcome these limitations, we propose the language-sensitive intervention (LSI), a novel, lightweight, and label-free approach. Specifically, we analyze language confusion from a causal perspective, revealing that the training corpus`s language distribution acts as a confounder, disadvantaging languages that are underrepresented in the dataset. Then, we identify a language-sensitive dimension in the LLM`s residual stream, i.e., the language vector, which allows us to estimate the average causal effect of prompts on this dimension. During inference, we directly intervene on the language vector to generate responses in the desired language.To further advance research on this issue, we introduce a new benchmark that detects language confusion and assesses content quality. Experimental results demonstrate that our method effectively mitigates language confusion without additional complex mechanisms. Our code is available at https://github.com/SoseloX/LSI.