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Ensuring factual consistency in summarization remains a challenge, especially for long-document evaluation. While automated, reference-free evaluation models are essential given the impracticality of large-scale human assessment for lengthy texts, challenges persist in evaluating different systems on how to handle different summary granularities and evolving model generations. In this work, we conduct a systematic study on diverse factual-consistency evaluation systems across four long-document datasets, encompassing summaries generated by models from non-LLMs to proprietary LLMs. Our analysis reveals that fine-grained continuous scores can provide more reliable assessments of different evaluation systems&#39; capabilities than binary classification. We also examine the relationship between sentence-level and summary-level model performance, highlighting its dependency on dataset characteristics. Moreover, our study reveals that advanced systems can obtain higher recall on error detection in older summaries, yet struggle with false-positives and fine-grained error detection. Our study provides insights into designing robust factuality evaluation systems, which are becoming more in demand as generative models continue to advance rapidly.

ACL 2025

A Tale of Evaluating Factual Consistency: Case Study on Long Document Summarization Evaluation

evaluation methodologies

long-form summarization

factuality

evaluation

metrics

## Welcome to EMNLP 2022!
I am delighted to welcome you to EMNLP 2022! I believe this conference has been complicated beyond any precedent. Over the past year, it’s been thrilling to see the organization team approach each new puzzle with creativity and enthusiasm. We hope that those participating in Abu Dhabi as well as those joining remotely will leave the conference feeling newly inspired by the program and newly connected to our ever-growing community. Following EMNLP 2021 and major NLP conferences since, EMNLP 2022 is “hybrid,” serving both virtual and in-person participants.

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EMNLP 2022

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EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

Ensuring factual consistency in summarization remains a challenge, especially for long-document evaluation. While automated, reference-free evaluation models are essential given the impracticality of large-scale human assessment for lengthy texts, challenges persist in evaluating different systems on how to handle different summary granularities and evolving model generations. In this work, we conduct a systematic study on diverse factual-consistency evaluation systems across four long-document datasets, encompassing summaries generated by models from non-LLMs to proprietary LLMs. Our analysis reveals that fine-grained continuous scores can provide more reliable assessments of different evaluation systems' capabilities than binary classification. We also examine the relationship between sentence-level and summary-level model performance, highlighting its dependency on dataset characteristics. Moreover, our study reveals that advanced systems can obtain higher recall on error detection in older summaries, yet struggle with false-positives and fine-grained error detection. Our study provides insights into designing robust factuality evaluation systems, which are becoming more in demand as generative models continue to advance rapidly.

poster

### 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)

The scaling of causal language models in size and training data enabled them to tackle increasingly complex tasks. Despite the development of sophisticated tests to reveal their new capabilities, the underlying basis of these complex skills remains unclear. We argue that complex skills might be explained using simpler ones, represented by linguistic concepts. As an initial step in exploring this hypothesis, we focus on the lexical-semantic concept of synonymy, laying the groundwork for research into its relationship with more complex skills. We develop a comprehensive test suite to assess various aspects of synonymy under different conditions, and evaluate causal open-source models. We find that these models effectively recognize synonymy but struggle to generate synonyms when prompted with relevant context.

Evaluating Pretrained Causal Language Models for Synonymy

With a statistical analysis of arXiv paper abstracts, we report a marked drop in the frequency of several words previously identified as overused by ChatGPT, such as "delve", starting soon after they were pointed out in early 2024. The frequency of certain other words favored by ChatGPT, such as "significant", has instead kept increasing. These phenomena suggest that some authors of academic papers have adapted their use of large language models (LLMs), for example, by selecting outputs or applying modifications to the LLM-generated content. Such coevolution and cooperation of humans and LLMs thus introduce additional challenges to the detection of machine-generated text in real-world scenarios. Estimating the impact of LLMs on academic writing by examining word frequency remains feasible, and more attention should be paid to words that were already frequently employed, including those that have decreased in frequency due to LLMs' disfavor.

Human-LLM Coevolution: Evidence from Academic Writing

We investigate whether the success of a zero-shot Chain-of-Thought (CoT) process can be predicted before completion. Our classifier, based on LLM representations, performs well even before a single token is generated, suggesting that crucial information about the reasoning process is already present in the initial steps representations. In contrast, a strong BERT-based baseline, which relies solely on the generated tokens, performs worse—likely because it depends on shallow linguistic cues rather than deeper reasoning dynamics. Surprisingly, using later reasoning steps does not always improve classification. When additional context is unhelpful, earlier representations resemble later ones more, suggesting LLMs encode key information early. This implies reasoning can often stop early without loss. To test this, we conduct early stopping experiments, showing that truncating CoT reasoning still improves performance over not using CoT at all, though a gap remains compared to full reasoning. However, approaches like supervised learning or reinforcement learning designed to shorten CoT chains could leverage our classifier’s guidance to identify when early stopping is effective. Our findings provide insights that may support such methods, helping to optimize CoT’s efficiency while preserving its benefits.

Knowing Before Saying: LLM Representations Encode Information About Chain-of-Thought Success Before Completion

Taxonomies are hierarchical knowledge graphs crucial for recommendation systems, and web applications. As data grows, expanding taxonomies is essential, but existing methods face key challenges: (1) discriminative models struggle with representation limits and generalization, while (2) generative methods either process all candidates at once, introducing noise and exceeding context limits, or discard relevant entities by selecting noisy candidates. We propose LORex (Lineage-Oriented Reasoning for Taxonomy Expansion), a plug-and-play framework that combines discriminative ranking and generative reasoning for efficient taxonomy expansion. Unlike prior methods, LORex ranks and chunks candidate terms into batches, filtering noise and iteratively refining selections by reasoning candidates’ hierarchy to ensure contextual efficiency. Extensive experiments across four benchmarks and twelve baselines show that LORex improves accuracy by 12% and Wu & Palmer similarity by 5% over state-of-the-art methods.

Rank, Chunk and Expand: Lineage-Oriented Reasoning for Taxonomy Expansion

Transformers have achieved state-of-the-art performance in morphological inflection tasks, yet their ability to generalize across languages and morphological rules remains limited. One possible explanation for this behavior can be the degree to which these models are able to capture implicit phenomena at the phonological and subphonemic levels. We introduce a language-agnostic probing method to investigate phonological feature encoding in transformers trained directly on phonemes, and perform it across seven morphologically diverse languages. We show that phonological features which are local, such as final-obstruent devoicing in Turkish, are captured well in phoneme embeddings, whereas long-distance dependencies like vowel harmony are better represented in the transformer's encoder. Finally, we discuss how these findings inform empirical strategies for training morphological models, particularly regarding the role of subphonemic feature acquisition.

Probing Subphonemes in Morphology Models

Amnesic probing is a method that can be used to investigate whether specific linguistic information has impact on the behavior of a model. The idea is to identify and remove information. We can then test whether removing this information leads to different performance on a main task. If the information is relevant for the task, performance should drop. The difficulty in this approach is removing only the target information while leaving other information unchanged. It has been shown that Iterative Nullspace Projection (INLP), a commonly used removal method, introduces random modifications to the representations when removing the target information. We show that Mean Projection (MP) and LEACE, suggested alternatives, remove information in a more targeted way and increases the possibilities of obtaining behavioral explanations through Amnesic Probing.

Improving Causal Interventions in Amnesic Probing with Mean Projection

Large language models (LLMs) show promising capabilities in predicting human emotions from text. However, the mechanisms through which these models process emotional stimuli remain largely unexplored. Our study addresses this gap by investigating how autoregressive LLMs infer emotions, showing that emotion representations are functionally localized to specific regions in the model. Our evaluation includes diverse model families and sizes, and is supported by robustness checks. We then show that the identified representations are psychologically plausible by drawing on cognitive appraisal theory—a well-established psychological framework positing that emotions emerge from evaluations (appraisals) of environmental stimuli. By causally intervening on construed appraisal concepts, we steer the generation and show that the outputs align with theoretical and intuitive expectations. This work highlights a novel way to causally intervene and control emotion inference, potentially benefiting safety and alignment in sensitive affective domains.

Mechanistic Interpretability of Emotion Inference in Large Language Models

In face-to-face interaction, we use multiple modalities, including speech and gestures, to communicate information and resolve references to objects. However, how representational co-speech gestures refer to objects remains understudied from a computational perspective. In this work, we address this gap by introducing a multimodal reference resolution task centred on representational gestures, while simultaneously tackling the challenge of learning robust gesture embeddings. We propose a self-supervised pre-training approach to gesture representation learning that grounds body movements in spoken language. Our experiments show that the learned embeddings align with expert annotations and have significant predictive power. Moreover, reference resolution accuracy further improves when (1) using multimodal gesture representations, even when speech is unavailable at inference time, and (2) leveraging dialogue history. Overall, our findings highlight the complementary roles of gesture and speech in reference resolution, offering a step towards more naturalistic models of human-machine interaction.

I see what you mean: Co-Speech Gestures for Reference Resolution in Multimodal Dialogue

Annotating event descriptions with their aspectual features is often seen as a pre-requisite to temporal reasoning. However, a recent study by Pruś et al. (2024) has shown that non-experts' intuitions on the aspectual class of English verb phrases can differ both from expert linguistic annotations and one another. They suggested that people tacitly conjuring their own contexts is what led them to disagreement between them. In this paper, we examine that suggestion by adding context to their examples and mirroring their experiment. Our result show that whilst the suggestion explains some of the disagreement, some examples yielded divided responses even with the additional context. We therefore suggest that individual's experience in the real world might shape how they interpret action descriptions. Finally, we show that outputs from GPT-4, despite to some degree capturing the aspectual class division, are not an accurate predictor of human answers.

World Knowledge Resolves Aspectual Ambiguity

Transformer-based large language models (LLMs) rely on contextual embeddings which generate different (continuous) representations for the same token depending on its surrounding context. Nonetheless, words and tokens typically have a limited number of senses (or meanings). We propose multi-sense embeddings as a drop-in replacement for each token in order to capture the range of their uses in a language. To construct a sense embedding dictionary, we apply a clustering algorithm to embeddings generated by an LLM and consider the cluster centers as representative sense embeddings. In addition, we propose a novel knowledge distillation method that leverages the sense dictionary to learn a smaller student model that mimics the senses from the much larger base LLM model, offering significant space and inference time savings, while maintaining competitive performance. Via thorough experiments on various benchmarks, we showcase the effectiveness of our sense embeddings and knowledge distillation approach.

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Evaluating Pretrained Causal Language Models for Synonymy

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