Austria

LLMs exhibit promising Social Intelligence (SI) in modeling human behavior, raising the need to evaluate LLMs&#39; SI and their discrepancy with humans. SI equips humans with interpersonal abilities to behave wisely in navigating social interactions to achieve social goals. This presents an operational evaluation paradigm: outcome-oriented goal achievement evaluation and process-oriented interpersonal ability evaluation, which existing work fails to address. To this end, we propose SocialEval, a script-based bilingual SI benchmark, integrating outcome- and process-oriented evaluation by manually crafting narrative scripts. Each script is structured as a world tree that contains plot lines driven by interpersonal ability, providing a comprehensive view of how LLMs navigate social interactions. Experiments show that LLMs fall behind humans on both SI evaluations, exhibit prosociality, and prefer more positive social behaviors, even if they lead to goal failure. Analysis of LLMs&#39; formed representation space and neuronal activations reveals that LLMs have developed ability-specific functional partitions akin to the human brain.

ACL 2025

SocialEval: Evaluating Social Intelligence of Large Language Models

social intelligence

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ACL 2024

The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

LLMs exhibit promising Social Intelligence (SI) in modeling human behavior, raising the need to evaluate LLMs' SI and their discrepancy with humans. SI equips humans with interpersonal abilities to behave wisely in navigating social interactions to achieve social goals. This presents an operational evaluation paradigm: outcome-oriented goal achievement evaluation and process-oriented interpersonal ability evaluation, which existing work fails to address. To this end, we propose SocialEval, a script-based bilingual SI benchmark, integrating outcome- and process-oriented evaluation by manually crafting narrative scripts. Each script is structured as a world tree that contains plot lines driven by interpersonal ability, providing a comprehensive view of how LLMs navigate social interactions. Experiments show that LLMs fall behind humans on both SI evaluations, exhibit prosociality, and prefer more positive social behaviors, even if they lead to goal failure. Analysis of LLMs' formed representation space and neuronal activations reveals that LLMs have developed ability-specific functional partitions akin to the human brain.

technical paper

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

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*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.* 
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Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Controlling the output of Large Language Models (LLMs) through context-sensitive constraints has emerged as a promising approach to overcome the limitations of Context-Free Grammars (CFGs) in guaranteeing generation validity. However, such constraints typically require manual specification---a significant barrier demanding specialized expertise. We introduce a framework that automatically learns context-sensitive constraints from LLM interactions through a two-phase process: syntactic exploration to gather diverse outputs for constraint learning, followed by constraint exploitation to enforce these learned rules during generation. Experiments demonstrate that our method enables even small LLMs (1B parameters) to learn and generate with perfect constraint adherence, outperforming larger counterparts and state-of-the-art reasoning models. This work represents the first integration of context-sensitive grammar learning with LLM generation, eliminating manual specification while maintaining generation validity.

Learning and Enforcing Context-Sensitive Control for LLMs

The success of Transformer-based language models in NLP has sparked debate about their ability to simulate human language learning. Chomsky contends that these models indiscriminately acquire both natural and "impossible" languages. While recent studies have challenged this claim, the capacity of Transformers to handle unconventional linguistic structures remains underexplored.
Inspired by natural and speculative languages with circular structural properties, this study examines the ability of GPT-2 to learn languages featuring circular schemes. We synthesize such circular languages by mapping original sequences onto textual circles and then relinearize them using parametric, mathematically invertible procedures that "unwrap" the circles into linear sequences.
We train GPT-2 models on these relinearized corpora and assess the impact of linearization parameters by tracking structural distortion and measuring perplexity. Interestingly, high levels of distortion relative to the original structures do not necessarily correspond to increased perplexity, suggesting that GPT-2 is relatively insensitive to global token order during language acquisition. Instead, preserving local context during linearization plays a more critical role in model learning. Further analysis using surprisal differences reveals that positional shifts pose greater challenges to the model than changes in stride or direction, underscoring the nuanced effects of linearization strategies. These findings offer new insights into the inductive biases of Transformer-based models in acquiring unconventional linguistic structures.

Unwrapping Circularity: Can Transformers Learn Languages with Circular Schemes?

Large Language Models (LLMs) are typically trained to predict the next token in a sequence. However, their internal representations often encode signals that go beyond immediate next-token prediction. In this work, we investigate whether these hidden states also carry information about the remaining length of the generated output—an implicit form of foresight \cite{pal-etal-2023-future}. We formulate this as a regression problem where, at generation step $t$, the target is the number of remaining tokens $y_t = T - t$, with $T$ as the total output length.

We propose two approaches: (1) an aggregation-based model that combines hidden states from multiple transformer layers $\ell \in \{8, \dots, 15\}$ using element-wise operations such as mean or sum, and (2) a \textit{Layerwise Graph Regressor} that treats layerwise hidden states as nodes in a fully connected graph and applies a Graph Neural Network (GNN) to predict $y_t$. Both models operate on frozen LLM embeddings without requiring end-to-end fine-tuning.

Accurately estimating remaining output length has both theoretical and practical implications. From an interpretability standpoint, it suggests that LLMs internally track their generation progress. From a systems perspective, it enables optimizations such as output-length-aware scheduling \cite{shahout2024dontstopnowembedding}. Our graph-based model achieves state-of-the-art performance on the Alpaca dataset using LLaMA-3-8B-Instruct, reducing normalized mean absolute error (NMAE) by over 50\% in short-output scenarios.

When Will the Tokens End? Graph-Based Forecasting for LLMs Output Length

Most state-of-the-art large language models (LLMs) are trained mainly on English data, limiting their effectiveness on non-English, especially low-resource, languages. This study investigates whether language adapters can facilitate cross-lingual transfer in English-centric LLMs. We train language adapters for 13 languages using Llama 2 (7B) and Llama 3.1 (8B) as base models, and evaluate their effectiveness on two downstream tasks (MLQA and SIB-200) using either task adapters or in-context learning. Our results reveal that language adapters improve performance for languages not seen during pretraining, but provide negligible benefit for seen languages. These findings highlight the limitations of language adapters as a general solution for multilingual adaptation in English-centric LLMs.

Only for the Unseen Languages, Say the Llamas: On the Efficacy of Language Adapters for Cross-lingual Transfer in English-centric LLMs

Recent approaches to Hierarchical Text Classification (HTC) rely on capturing the global label hierarchy, which contains static and often redundant relationships. Instead, the hierarchical relationships within the instance-specific set of positive labels are more important, as they focus on the relevant parts of the hierarchy. These localized relationships can be modeled as a semantic alignment between the text and its positive labels within the embedding space. However, without explicitly encoding the global hierarchy, achieving this alignment directly in Euclidean space is challenging, as its flat geometry does not naturally support hierarchical
relationships. To address this, we propose Hyperbolic Instance-Specific Local Relationships (HyILR), which models instance-specific relationships using the Lorentz model of hyperbolic space. Text and label features are projected into hyperbolic space, where a contrastive loss aligns text with its labels. This loss is guided by a hierarchy-aware negative sampling strategy, ensuring the selection of structurally and semantically relevant negatives. By leveraging hyperbolic geometry for this alignment, our approach inherently captures hierarchical relationships and eliminates the need for global hierarchy encoding. Experimental results on four benchmark datasets validate the superior performance of HyILR over baseline methods.

HyILR: Hyperbolic Instance-Specific Local Relationships for Hierarchical Text Classification

Drawing on pragmatic theories of implicature by Grice (1975) and Levinson (1983), according to which speakers often convey more than it is explicitly said, the paper argues that interpreting texts with implicit meaning correctly is essential for precise sentiment analysis. To illustrate the challenges in computational interpretation of implicatures, the study introduces a series of illustrative micro-experiments with the use of four transformer models fine-tuned for sentiment analysis. In these micro-experiments, the models classified sentences specifically designed to expose difficulties in handling implicit meaning. The study demonstrates that contrasting qualitative pragmatic analysis with the models' tendency to focus on formal linguistic markers can reveal the limitations of supervised machine learning methods in detecting implicit sentiments.

Pragmatic Perspective on Assessing Implicit Meaning Interpretation in Sentiment Analysis Models

In education, peer instruction (PI) is widely recognized as an effective active learning strategy. However, real-world evaluations of PI are often limited by logistical constraints and variability in classroom settings. This paper introduces PEERS (Peer Enhanced Educational Realistic Simulation), a simulation framework that integrates Agent-Based Modeling (ABM), Large Language Models (LLMs), and Bayesian Knowledge Tracing (BKT) to emulate student learning dynamics. As an initial step, this study focuses on evaluating whether LLM-powered agents can effectively assume the roles of teachers and students within the simulation. Human evaluations and topic-based metrics show that LLMs can generate role-consistent and contextually appropriate classroom dialogues. These results serve as a foundational milestone toward building realistic, AI-driven educational simulations. Future work will include simulating the complete PEERS framework and validating its accuracy through actual classroom-based PI sessions. This research aims to contribute a scalable, cost-effective methodology for studying instructional strategies in controlled yet realistic environments.

Foundations of PEERS: Assessing LLM Role Performance in Educational Simulations

Integrating knowledge graphs (KGs) into the reasoning processes of large language models (LLMs) has emerged as a promising approach to mitigate hallucination. However, existing work in this area often relies on proprietary or extremely large models, limiting accessibility and scalability. In this study, we investigate the capabilities of existing integration methods for small language models (SLMs) in KG-based question answering and observe that their performance is often constrained by their limited ability to traverse and reason over knowledge graphs. To address this limitation, we propose leveraging simple and efficient exploration modules to handle knowledge graph traversal in place of the language model itself. Experiment results demonstrate that these lightweight modules effectively improve the performance of small language models on knowledge graph question answering tasks. Our code will be available on Github.

The Role of Exploration Modules in Small Language Models for Knowledge Graph Question Answering

To evaluate the creativity of large language models (LLMs) in Japanese, we construct three benchmarks: Japanese Creativity Questions (JCQ), Divergent Association Task (DAT), and Story Alteration Task (SAT). JCQ comprehensively evaluates creativity using LLMs. Meanwhile, DAT and SAT measure specific aspects of creative ability using embeddings. We also analyze correlations between JCQ and DAT, JCQ and SAT, and DAT and SAT. While JCQ provides comprehensive evaluation, it is relatively time and resource intensive. In contrast, DAT and SAT offer lower comprehensiveness but enable quick, low-cost assessment. Additionally, we investigate whether training with DAT contributes to enhancing LLM creativity.

Building Japanese Creativity Benchmarks and Applying them to Enhance LLM Creativity

Sentiment analysis in policy-related studies typically involves annotating a subset of data to fine-tune a pre-trained model, which is subsequently used to classify sentiments in the remaining unlabeled texts, enabling policy researchers to analyze sentiments in novel policy contexts under resource constraints. We argue that existing methods fail to adequately capture the temporal volatility inherent in policy-related sentiments, which are subject to external shocks and evolving discourse of opinions. We propose methods accounting for the temporal dynamics of policy-related texts. Specifically, we propose leveraging continuous time-series clustering to select data points for annotation based on temporal trends and subsequently apply model merging techniques -- each fine-tuned separately on data from distinct time intervals. Our results indicate that continuous time-series clustering followed by fine-tuning a single unified model achieves superior performance, outperforming existing methods by an average F1-score of 2.71\%. This suggests that language models can generalize to temporally sensitive texts when provided with temporally representative samples. Nevertheless, merging multiple time-specific models - particularly via greedy soup and TIES - achieves competitive performance, suggesting practical applications in dynamically evolving policy scenarios.

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