United States

We propose a novel framework to generate causal graphs from narrative texts, bridging the gap between high-level causality and finer-grained event-specific relationships. Our approach first extracts concise, agent-centered &quot;vertices&quot; using an LLM-based summarization strategy. We then introduce an *Expert Index*—seven linguistically grounded features—and incorporate them into a **STAC** (Situation, Task, Action, Consequence) classification model. This hybrid system (RoBERTa embeddings + Expert Index) achieves superior precision in identifying causal links compared to LLM-only baselines. Finally, we apply a structured, five-iteration prompting process to refine and construct a connected causal graph. Experiments on 100 chapters and short stories show that our method consistently outperforms GPT-4o and Claude 3.5 across key dimensions of causal graph quality, while maintaining comparable readability. The resulting open-source tool offers an interpretable and efficient solution for capturing nuanced causal chains within narrative texts.

NAACL 2025

Beyond LLMs A Linguistic Approach to Causal Graph Generation from Narrative Texts

narrative story

language understanding

computational linguistics

causal graph

**Welcome to ACL-IJCNLP 2021!**

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP). 

As in previous years, the program of the conference includes a poster session, tutorials, workshops and demonstrations in addition to the main conference.


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ACL-IJCNLP 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

We propose a novel framework to generate causal graphs from narrative texts, bridging the gap between high-level causality and finer-grained event-specific relationships. Our approach first extracts concise, agent-centered "vertices" using an LLM-based summarization strategy. We then introduce an *Expert Index*—seven linguistically grounded features—and incorporate them into a **STAC** (Situation, Task, Action, Consequence) classification model. This hybrid system (RoBERTa embeddings + Expert Index) achieves superior precision in identifying causal links compared to LLM-only baselines. Finally, we apply a structured, five-iteration prompting process to refine and construct a connected causal graph. Experiments on 100 chapters and short stories show that our method consistently outperforms GPT-4o and Claude 3.5 across key dimensions of causal graph quality, while maintaining comparable readability. The resulting open-source tool offers an interpretable and efficient solution for capturing nuanced causal chains within narrative texts.

workshop paper

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
**[Continue reading...](https://drive.google.com/file/d/1jX-qGhqVSZZCIrAnJaz798pflu5Irrdn/view?usp=sharing)**

*- Colin Cherry, Google, NAACL 2025 General Chair* 

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Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Interactive storytelling benefits from planning and exploring multiple ``what if'' scenarios. Modern LLMs are useful tools for ideation and exploration, but current chat-based user interfaces restrict users to a single linear flow. To address this limitation, we propose Narrative Studio -- a novel in-browser narrative exploration environment featuring a tree-like interface that allows branching exploration from user-defined points in a story. Each branch is extended via iterative LLM inference guided by system and user-defined prompts. Additionally, we employ Monte Carlo Tree Search (MCTS) to automatically expand promising narrative paths based on user-specified criteria, enabling more diverse and robust story development. We also allow users to enhance narrative coherence by grounding the generated text in a graph that represents the actors and environment of the story.

Narrative Studio: Visual narrative exploration using LLMs and Monte Carlo Tree Search

Recent advancements in language representation learning primarily emphasize language modeling for deriving meaningful representations, often neglecting style-specific considerations. This study addresses this gap by creating generic, sentence-level style embeddings crucial for style-centric tasks. Our approach is grounded on the premise that low-level text style changes can compose any high-level style. We hypothesize that applying this concept to representation learning enables the development of versatile text style embeddings. By fine-tuning a general-purpose text encoder using contrastive learning and standard cross-entropy loss, we aim to capture these low-level style shifts, anticipating that they offer insights applicable to high-level text styles. The outcomes prompt us to reconsider the underlying assumptions as the results do not always show that the learned style representations capture high-level text styles.

Challenging Assumptions in Learning Generic Text Style Embeddings

Sequence-to-sequence models are widely used to train Abstract Meaning Representation (Banarescu et al.,2013, AMR) parsers. To train such models, AMR graphs have to be linearized into a one-line text format. While Penman encoding is widely used for this purpose, we argue that it has limitations: 1) for deep graphs, some closely related nodes are located far apart in the linearized text 2) Penman's tree-based encoding necessitates inverse roles to handle node re-entrancy, doubling the number of relation types to predict. To address these issues, we propose a triple-based linearization method and compare its efficiency by training an AMR parser with both approaches. Although triple is well suited to represent a graph, our results show that it does not yet improve performance on deeper or longer graphs. It suggests room for improvement in its design to better compete with Penman's concise representation and explicit encoding of a nested graph structure.

Reassessing Graph Linearization for Sequence-to-sequence AMR Parsing: On the Advantages and Limitations of Triple-Based

In-context learning (ICL) has transformed the use of large language models (LLMs) for NLP tasks, enabling few-shot learning by conditioning on labeled examples without finetuning. Despite its effectiveness, ICL is prone to errors, especially for challenging examples. With the goal of improving the performance of ICL, we propose *corrective in-context learning* (CICL), an approach that incorporates a model's incorrect predictions alongside ground truth corrections into the prompt, aiming to enhance classification accuracy through self-correction. However, contrary to our hypothesis, extensive experiments on text classification tasks demonstrate that CICL consistently underperforms standard ICL, with performance degrading as the proportion of corrections in the prompt increases. Our findings indicate that CICL introduces confusion by disrupting the model's task understanding, rather than refining its predictions. Additionally, we observe that presenting harder examples in standard ICL does not improve performance, suggesting that example difficulty alone may not be a reliable criterion for effective selection. By presenting these negative results, we provide important insights into the limitations of self-corrective mechanisms in LLMs and offer directions for future research.

Corrective In-Context Learning: Evaluating Self-Correction in Large Language Models

Multilingual large language models (LLMs) aim towards robust natural language understanding across diverse languages, yet their performance significantly degrades on low-resource languages. This work explores whether existing techniques to identify language-specific neurons can be leveraged to enhance cross-lingual task performance of low-resource languages. We conduct detailed experiments covering existing language-specific neuron identification techniques (such as Language
Activation Probability Entropy and activation probability-based thresholding) and
neuron-specific LoRA fine-tuning with models like Llama 3.1 and Mistral Nemo. We find that such neuron-specific interventions are insufficient to yield cross-lingual improvements on downstream tasks (XNLI, XQuAD) in low-resource languages. This study highlights the challenges in achieving cross-lingual generalization and provides critical insights for multilingual LLMs.

Language-Specific Neurons Do Not Facilitate Cross-Lingual Transfer

Intermediate Layer Distillation (ILD) is a variant of Knowledge Distillation (KD), a method for compressing neural networks.
ILD requires mapping to align the intermediate layer sizes of the teacher and student models to compute the loss function in training, while this mapping is not used during inference.
This inconsistency may reduce the effectiveness of learning in intermediate layers.
In this study, we propose LoRAILD, which uses LoRA adapters to eliminate the inconsistency.
However, our experimental results show that LoRAILD does not outperform existing methods.
Furthermore, contrary to previous studies, we observe that conventional ILD does not outperform vanilla KD.
Our analysis of the distilled models' intermediate layers suggests that ILD does not improve language models' performance.

Aligning Sizes of Intermediate Layers by LoRA Adapter for Knowledge Distillation

Sustainability metrics have increasingly become a crucial non-financial criterion in investment decision-making. Organizations worldwide are recognizing the importance of sustainability and are proactively highlighting their efforts through specialized sustainability reports. Unlike traditional annual reports, these sustainability disclosures are typically text-heavy and are often expressed as infographics, complex tables, and charts. The non-machine-readable nature of these reports presents a significant challenge for efficient information extraction. The rapid advancement of Vision Language Models (VLMs) has raised the question whether these VLMs can address such challenges in domain specific task. In this study, we demonstrate the application of VLMs for extracting sustainability information from dedicated sustainability reports. Our experiments highlight the limitations in the performance of several open-source VLMs in extracting information about sustainability disclosures from different type of pages.

Exploring Multimodal Language Models for Sustainability Disclosure Extraction: A Comparative Study

Large Language Models (LLMs) enhanced with tool use and APIs improve task performance but often misuse them, leading to inefficiency and unnecessary cost. We propose Self Knowledge-Tracing for Tool Use (SKT-Tool), a method enabling LLMs to assess their capabilities and make informed API usage decisions using knowledge tracing (KT). Our teacher-student framework helps LLMs optimize API calls in real-time without fine-tuning. Experiments across multiple datasets show that SKT-Tool significantly reduces API calls while maintaining accuracy, offering a scalable and cost-effective solution for tool-augmented LLMs. We conclude by analyzing shortcomings in this method and identifying directions for future work.

Self Knowledge-Tracing for Tool Use (SKT-Tool): Helping LLM Agents Understand Their Capabilities in Tool Use

Evaluating an LLM's robustness against numerical perturbation is a good way to know if the LLM actually performs reasoning or just replicates patterns learned. We propose a novel method to augment math word problems (MWPs), producing numerical variations at a large scale utilizing templates. We also propose an automated error classification framework for scalable error analysis, distinguishing calculation errors from reasoning errors. Our experiments using the methods show LLMs are weak against numerical variations, suggesting they are not fully capable of generating valid reasoning steps, often failing in arithmetic operations.

Evaluating Robustness of LLMs to Numerical Variations in Mathematical Reasoning

In this work, we reimagine classical probing to evaluate knowledge transfer from simple source to more complex target tasks. Instead of probing frozen representations from a complex source task on diverse simple target probing tasks (as usually done in probing), we explore the effectiveness of embeddings from multiple simple source tasks on a single target task. We select coreference resolution, a linguistically complex problem requiring contextual understanding, as focus target task, and test the usefulness of embeddings from comparably simpler tasks tasks such as paraphrase detection, named entity recognition, and relation extraction. Through systematic experiments, we evaluate the impact of individual and combined task embeddings.

Our findings reveal that task embeddings vary significantly in utility for coreference resolution, with semantic similarity tasks (e.g., paraphrase detection) proving most beneficial. Additionally, representations from intermediate layers of fine-tuned models often outperform those from final layers. Combining embeddings from multiple tasks consistently improves performance, with attention-based aggregation yielding substantial gains. These insights shed light on relationships between task-specific representations and their adaptability to complex downstream tasks, encouraging further exploration of embedding-level task transfer. Our source code is publicly available under https://github.com/Cora4NLP/multi-task-knowledge-transfer.

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