Austria

Long-context language models (LCLMs) have exhibited impressive capabilities in long-context understanding tasks. Among these, long-context referencing—a crucial task that requires LCLMs to attribute items of interest to specific parts of long-context data—remains underexplored. To bridge this gap, this paper proposes Referencing Evaluation for Long-context Language Models (Ref-Long), a novel benchmark designed to assess the long-context referencing capability of LCLMs. Specifically, Ref-Long requires LCLMs to identify the indexes of documents that reference a specific key, emphasizing contextual relationships between the key and the documents over simple retrieval. Based on the task design, we construct three subsets ranging from synthetic to realistic scenarios to form the Ref-Long benchmark. Experimental results of 13 LCLMs reveal significant shortcomings in long-context referencing, even among advanced models like GPT-4o. To further investigate these challenges, we conduct comprehensive analyses, including human evaluations, task format adjustments, fine-tuning experiments, and error analyses, leading to several key insights. Our data and code will be publicly released, and the data is also attached in the submission.

ACL 2025

Ref-Long: Benchmarking the Long-context Referencing Capability of Long-context Language Models

referencing

long-context understanding

long-context language models

evaluation

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*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)

poster

Linguistic coordination—a phenomenon where conversation partners end up having similar patterns of language use—has been established across a variety of contexts and for multiple linguistic features. However, the study of language coordination has been accompanied by a diverse and inconsistently applied set of measures and theoretical perspectives. This diversity has significant consequences, as replication studies have highlighted the brittleness of certain measures and called influential findings into question. While prior work has addressed specific modeling decisions and model types, linguistic coordination research has yet to fully examine, synthesize, and critique the space of modeling choices available. In this work, we present a framework to organize the linguistic coordination literature. Using this schema, we provide a high-level overview of the choices involved in the measurement process and synthesize relevant critiques. Based on both gaps and limitations surfaced from this review, we suggest directions for further exploration and evaluation. In doing so, we provide the clarity required for linguistic coordination research to arrive at interpretable and sound conclusions.

Coordinating Chaos: A Structured Review of Linguistic Coordination Methodologies

While Transformer models have achieved remarkable success in various domains, the effectiveness of information propagation through deep networks remains a critical challenge. Standard hidden state residuals often fail to adequately preserve initial token-level information in deeper layers. This paper introduces ResFormer, a novel architecture that enhances information flow by incorporating value residual connections in addition to hidden state residuals. And a variant is SVFormer, where all layers share the first layer's value embedding. Comprehensive empirical evidence demonstrates ResFormer achieves equivalent validation loss with 16.11\% fewer model parameters and 20.3\% less training data compared to Transformer, while maintaining similar memory usage and computational cost. Besides, SVFormer reduces KV cache size by nearly half with only a small performance penalty and can be integrated with other KV-efficient methods, yielding further reductions in KV cache, with performance influenced by sequence length and cumulative learning rate.

Value Residual Learning

Large Language Model (LLM) Uncertainty Estimation (UE) methods have become a crucial tool for detecting hallucinations in recent years. While numerous UE methods have been proposed, most existing studies evaluate them in isolated short-form QA settings using threshold-independent metrics such as AUROC or PRR. However, real-world deployment of UE methods introduces several challenges. In this work, we systematically examine four key aspects of deploying UE methods in practical settings. Specifically, we assess (1) the sensitivity of UE methods to decision threshold selection, (2) their robustness to query transformations such as typos, adversarial prompts, and prior chat history, (3) their applicability to long-form generation, and (4) strategies for handling multiple UE scores for a single query. Our evaluations on 19 UE methods reveal that most of them are highly sensitive to threshold selection when there is a distribution shift in the calibration dataset. While these methods generally exhibit robustness against previous chat history and typos, they are significantly vulnerable to adversarial prompts. Additionally, while existing UE methods can be adapted for long-form generation through various strategies, there remains considerable room for improvement. Lastly, ensembling multiple UE scores at test time provides a notable performance boost, which highlights its potential as a practical improvement strategy. Code is available at: https://github.com/duygunuryldz/uncertainty_in_the_wild.

Reconsidering LLM Uncertainty Estimation Methods in the Wild

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.

SocialEval: Evaluating Social Intelligence of Large Language Models

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.

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