Austria

Historical analogies, which compare known past events with contemporary but unfamiliar events, are important abilities that help people make decisions and understand the world. However, research in applied history suggests that people have difficulty finding appropriate analogies. And previous studies in the AI community have also overlooked historical analogies. To fill this gap, in this paper, we focus on the historical analogy acquisition task, which aims to acquire analogous historical events for a given event. We explore retrieval and generation methods for acquiring historical analogies based on different large language models (LLMs). Furthermore, we propose a self-reflection method to mitigate hallucinations and stereotypes when LLMs generate historical analogies. Through human evaluations and our specially designed automatic multi-dimensional assessment, we find that LLMs generally have a good potential for historical analogies. And the performance of the models can be further improved by using our self-reflection method. Resources of this paper can be found at https://anonymous.4open.science/r/Historical-Analogy-of-LLMs-FC17

ACL 2025

Past Meets Present: Creating Historical Analogy with Large Language Models

historical analogy

dataset source

evaluation methodology

### Welcome to CogSci Conference 2025!

The 47th Annual Meeting of the Cognitive Science Society was a hybrid meeting held in San Francisco. 

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CogSci 2025

The 47th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

technical paper

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair:<br>
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for  Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025.  ACL2025continues our field’s tradition of excellence in scholarship, innovation, and inclusivity, and I am  deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.*<br>
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Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Recent advancements in visual generative models have enabled high-quality image and video generation, opening diverse applications. However, evaluating these models often demands sampling hundreds or thousands of images or videos, making the process computationally expensive, especially for diffusion-based models with inherently slow sampling. Moreover, existing evaluation methods rely on rigid pipelines that overlook specific user needs and provide numerical results without clear explanations. In contrast, humans can quickly form impressions of a model's capabilities by observing only a few samples. To mimic this, we propose the Evaluation Agent framework, which employs human-like strategies for efficient, dynamic, multi-round evaluations using only a few samples per round, while offering detailed, user-tailored analyses. It offers four key advantages: 1) efficiency, 2) promptable evaluation tailored to diverse user needs, 3) explainability beyond single numerical scores, and 4) scalability across various models and tools. Experiments show that Evaluation Agent reduces evaluation time to 10% of traditional methods while delivering comparable results. The Evaluation Agent framework is fully open-sourced to advance research in visual generative models and their efficient evaluation.

Evaluation Agent: Efficient and Promptable Evaluation Framework for Visual Generative Models

Multimodel Large Language Models(MLLMs) have achieved promising OCR-free Document Understanding performance by increasing the supported resolution of document images. However, this comes at the cost of generating thousands of visual tokens for a single document image, leading to excessive GPU memory and slower inference times, particularly in multi-page document comprehension. In this work, to address these challenges, we propose a High-resolution DocCompressor module to compress each high-resolution document image into 324 tokens, guided by low-resolution global visual features. With this compression module, to strengthen multi-page document comprehension ability and balance both token efficiency and question-answering performance, we develop the DocOwl2 under a three-stage training framework: Single-image Pretraining, Multi-image Continue-pretraining, and Multi-task Finetuning. DocOwl2 sets a new state-of-the-art across multi-page document understanding benchmarks and reduces first token latency by more than 50%. Compared to single-image MLLMs trained on similar data, our DocOwl2 achieves comparable single-page understanding performance with less than 20% of the visual tokens. Our codes, models, and data will be publicly available.

mPLUG-DocOwl2: High-resolution Compressing for OCR-free Multi-page Document Understanding

While long-context inference is crucial for advancing large language model (LLM) applications, its prefill speed remains a significant bottleneck. Current approaches, including sequence parallelism strategies and compute reduction through approximate attention mechanisms, still fall short of delivering optimal inference efficiency. This hinders scaling the inputs to longer sequences and processing long-context queries in a timely manner. To address this, we introduce APB, an efficient long-context inference framework that leverages multi-host approximate attention to enhance prefill speed by reducing compute and enhancing parallelism simultaneously. APB introduces a communication mechanism for essential key-value pairs within a sequence parallelism framework, enabling a faster inference speed while maintaining task performance. We implement APB by incorporating a tailored FlashAttn kernel alongside optimized distribution strategies, supporting diverse models and parallelism configurations. APB achieves speedups of up to 9.2$\times$, 4.2$\times$, and 1.6$\times$ compared with FlashAttn, RingAttn, and StarAttn, respectively, without any observable task performance degradation.

APB: Accelerating Distributed Long-Context Inference by Passing Compressed Context Blocks across GPUs

Ensuring awareness of fairness and privacy in Large Language Models (LLMs) is critical. Interestingly, we discover a counter-intuitive trade-off phenomenon that enhancing an LLM's privacy awareness through Supervised Fine-Tuning (SFT) methods significantly decreases its fairness awareness with thousands of samples. To address this issue, inspired by the information theory, we introduce a training-free method to \textbf{S}uppress the \textbf{P}rivacy and fa\textbf{I}rness coupled \textbf{N}eurons (\textbf{SPIN}), which theoretically and empirically decrease the mutual information between fairness and privacy awareness. Extensive experimental results demonstrate that SPIN eliminates the trade-off phenomenon and significantly improves LLMs' fairness and privacy awareness simultaneously without compromising general capabilities, e.g., improving Qwen-2-7B-Instruct's fairness awareness by 12.2\% and privacy awareness by 14.0\%.
More crucially, SPIN remains robust and effective with limited annotated data or even when only malicious fine-tuning data is available, whereas SFT methods may fail to perform properly in such scenarios. 
Furthermore, we show that SPIN could generalize to other potential trade-off dimensions.
We hope this study provides valuable insights into concurrently addressing fairness and privacy concerns in LLMs and can be integrated into comprehensive frameworks to develop more ethical and responsible AI systems. Our code is available at \url{https://github.com/ChnQ/SPIN}.

The Tug of War Within: Mitigating the Fairness-Privacy Conflicts in Large Language Models

While retrieval techniques are widely used in practice, they still face significant challenges in cross-domain scenarios. Recently, generation-augmented methods have emerged as a promising solution to this problem. These methods enhance raw queries by incorporating additional information from an LLM-based generator, facilitating more direct retrieval of relevant documents. However, existing methods struggle with highly specialized situations that require extensive domain expertise. To address this problem, we present \textbf{Reinforced-IR}, a novel approach that jointly adapts a pre-trained retriever and generator for precise cross-domain retrieval. A key innovation of Reinforced-IR is its \textbf{Self-Boosting} framework, which enables retriever and generator to learn from each other's feedback. Specifically, the generator is reinforced to generate query augmentations that enhance the retriever's performance, while the retriever is trained to better discriminate the relevant documents identified by the generator. This iterative process allows the end-to-end retrieval performance to be progressively optimized using an unlabeled corpus from the target domain. In our experiment, Reinforced-IR outperforms existing domain adaptation methods by a large margin, leading to substantial improvements in retrieval quality across a wide range of application scenarios.
We have publicly released our code at this \href{https://github.com/FlagOpen/FlagEmbedding/tree/master/research/Reinforced_IR}{repo}.

Reinforced IR: A Dual Reinforcement Framework For Domain-Adapted Information Retrieval

Existing approaches to fine-grained emotion classification (FEC) often operate in Euclidean space, where the flat geometry limits the ability to distinguish semantically similar emotion labels (e.g., *annoyed* vs. *angry*). While prior research has explored hyperbolic geometry to capture fine-grained label distinctions, it typically relies on predefined hierarchies and overlooks semantically confusable negatives. In this work, we propose HyCoEM, a semantic alignment framework that leverages the Lorentz model of hyperbolic space. Our approach jointly embeds text and label representations into hyperbolic space via the exponential map, and employs a contrastive loss to bring text embeddings closer to their true labels while pushing them away from adaptively selected, semantically similar negatives. This enables the model to learn label embeddings without relying on a predefined hierarchy and better captures subtle distinctions by incorporating information from both positive and challenging negative labels. Experimental results on two benchmark FEC datasets demonstrate the effectiveness of our approach over baseline methods.

Semantic alignment in hyperbolic space for fine-grained emotion classification

Modern Large Language Models (LLMs) have shown human-like abilities in many language tasks, sparking interest in comparing LLMs' and humans' language processing. In this paper, we try to answer two questions: 1. What makes garden-path sentences hard to understand for humans? 2. Do the same reasons make garden-path sentences hard for LLMs as well? Based on psycholinguistic research, we formulate hypotheses on why garden-path sentences are hard, and test these hypotheses on human participants and a large suite of LLMs using comprehension questions. Our findings reveal that both LLMs and humans struggle with specific syntactic complexities, with some models showing high correlation with human comprehension. To complement our findings, we test LLM comprehension of garden-path constructions with paraphrasing and text-to-image generation tasks, and find that the results mirror the sentence comprehension question results, further validating our findings on LLM understanding of these constructions.

When the LM misunderstood the human chuckled: Analyzing garden path effects in humans and language models

Language models are often miscalibrated, leading to confidently incorrect answers. We introduce GRACE, a benchmark for language model calibration that incorporates comparison with human calibration. GRACE consists of question-answer pairs, in which each question contains a series of clues that gradually become easier, all leading to the same answer; models must answer correctly as early as possible as the clues are revealed. This setting permits granular measurement of model calibration based on how early, accurately, and confidently a model answers. After collecting these questions, we host live human vs. model competitions to gather 1,749 data points on human and model teams' timing, accuracy, and confidence. We propose a metric, CalScore, that uses GRACE to analyze model calibration errors and identify types of model miscalibration that differ from human behavior. We find that although humans are less accurate than models, humans are generally better calibrated. Since state-of-the-art models struggle on GRACE, it effectively evaluates progress on improving model calibration.

GRACE: A Granular Benchmark for Evaluating Model Calibration against Human Calibration

Large language models (LLMs) have achieved remarkable progress in multilingual machine translation (MT), demonstrating strong performance even with limited parallel data. 
However, effectively fine-tuning LLMs for MT is challenging due to parameter interference, which arises from the conflicting demands of different language pairs and the risk of overwriting pre-trained knowledge. 
To address this issue, we propose \textbf{MLAS-LoRA}, a novel multiple language-aware LoRA knowledge transfer framework. 
MLAS-LoRA efficiently adapts LLMs to MT by selectively transferring knowledge from a large teacher to a small student model. 
Our approach first evaluates the awareness of neurons and extracts linguistic knowledge in the teacher model to both the general MT task and specific language pairs.
We then propose a multiple language-specific LoRA architecture to inject the extracted knowledge into the student model. 
During fine-tuning, only the parameters of the relevant language-general and language-specific LoRA modules are updated. 
Experimental results on diverse multilingual language pairs demonstrate that MLAS-LoRA significantly outperforms strong baselines by +1.7 BLEU on average, including standard fine-tuning and other parameter-efficient methods.

MLAS-LoRA: Language-Aware Parameters Detection and LoRA-Based Knowledge Transfer for Multilingual Machine Translation

Conditional dichotomy, the contrast between two outputs conditioned on the same context, is vital for applications such as debate, defeasible inference, and causal reasoning. Existing methods that rely on semantic similarity often fail to capture the nuanced oppositional dynamics essential for these applications. Motivated by these limitations, we introduce a novel task, Conditional Dichotomy Quantification (ConDQ), which formalizes the direct measurement of conditional dichotomy and provides carefully constructed datasets covering debate, defeasible natural language inference, and causal reasoning scenarios. To address this task, we develop the Dichotomy-oriented Geometric Embedding (DoGE) framework, which leverages complex-valued embeddings and a dichotomous objective to model and quantify these oppositional relationships effectively. Extensive experiments validate the effectiveness and versatility of DoGE, demonstrating its potential in understanding and quantifying conditional dichotomy across diverse NLP applications. Our code and datasets are available at https://github.com/cui-shaobo/conditional-dichotomy-quantification.

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Evaluation Agent: Efficient and Promptable Evaluation Framework for Visual Generative Models

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