Austria

Entailment trees are essential for enhancing interpretability and transparency in tasks like question answering and natural language understanding. However, existing approaches often lack logical consistency, as they rely on static reward structures or ignore the intricate dependencies within multi-step reasoning. To address these limitations, we propose a method that integrates natural logic principles into reinforcement learning, enabling dynamic reward computation to guide entailment tree generation. Our approach ensures logical consistency across reasoning steps while improving interpretability and generalization. Experiments on EntailmentBank demonstrate significant improvements over state-of-the-art methods, highlighting the effectiveness of natural logic in structured reasoning.

ACL 2025

Natural Logic at the Core: Dynamic Rewards for Entailment Tree Generation

entailment tree generation

natural logic

question answering

## Welcome to 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.

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### 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)

Natural language has been extensively used for modeling text-attributed graphs with LLMs. Natural language is used to describe the graph for LLMs to understand or serve as component of the graph, e.g., textual attributes for embedding generation. However, natural language is inherently redundant and unstructured, making it unsuitable for modeling high-order neighbors with LLMs. Specifically, (i) graph descriptions become verbose, overwhelming LLMs, and (ii) only relying on attribute embeddings limits LLM's ability to capture the adequate graph structural information. These limitations make it difficult to model graphs both concisely and adequately using sole natural language with LLMs.

Inspired by the observation that LLMs pre-trained on one language can achieve exceptional performance on another with minimal additional training, we propose Graph-Defined Language for Large Language Model (GDL4LLM). This novel framework enables LLMs to transfer their powerful language understanding capabilities to graph-structured data. GDL4LLM translates the graph into a graph language corpus instead of graph descriptions and pre-trains LLMs on this corpus to adequately understand the graph. This corpus represents the subgraph centered around target nodes concisely with only a few tokens during fine-tuning on downstream tasks. By treating the graph as a new language, GDL4LLM enables LLMs to model text-attributed graph adequately and concisely. Extensive experiments on five datasets demonstrate that GDL4LLM outperforms description-based and embedding-based baselines by efficiently modeling different orders of neighbors.

Each graph is a new language: Graph Learning with LLMs

In light of the widespread deployment of Large Language Models (LLMs), the responsibility for safeguarding and regulating LLM-generated content has taken on heightened significance. Recent advancements in LLM-based moderation methods, e.g., LlamaGuard, have demonstrated remarkable promise in identifying safety risks associated with both inputs and outputs in human-AI interactions. However, integrating LLM-based safeguards into a chatbot system requires an additional inference stage involving a moderation LLM with billions of parameters, which significantly increases computational costs and reduces overall efficiency. In this paper, we demonstrate that simply learning a classification head on the last-layer hidden states of the dialogue model provides a strong capability to identify harmful contents. The classification head, referred to as ShieldHead, serves as an auxiliary branch paralleled with next-token-prediction LM head, enabling the detection of potential risks in past text sequences. Additionally, a label disambiguation technique is employed to supervise ShieldHead with both token-level and sentence-level labels, which further enhances its performance. ShieldHead exhibits remarkable efficiency during inference, providing real-time moderation results alongside token-wise streaming output during the chatbot system's decoding phase. Extensive experimental results demonstrate the superiority of the proposed framework: a state-of-the-art performance on the XSTest and SafeRLHF datasets while running at a speed about **300×** faster (**<1ms**) than previous LLM-based moderation models with **~99%** less parameters of LlamaGuard.

ShieldHead: Decoding-time Safeguard for Large Language Models

Post-earnings volatility prediction is critical for investors, with previous works often leveraging earnings call transcripts under the assumption that their rich semantics contribute significantly. To further investigate how transcripts impact volatility, we introduce DEC, a dataset featuring accurate volatility calculations enabled by the previously overlooked beforeAfterMarket attribute and dense ticker coverage. Unlike established benchmarks, where each ticker has only around two earnings, DEC provides 20 earnings records per ticker. Using DEC, we reveal that post-earnings volatility undergoes significant shifts, with each ticker displaying a distinct volatility distribution. To leverage historical post-earnings volatility and capture ticker-specific patterns, we propose two training-free baselines: Post-earnings Volatility (PEV) and Same-ticker Post-earnings Volatility (STPEV). These baselines surpass all transcripts-based models on DEC as well as on established benchmarks. Additionally, we demonstrate that current transcript representations predominantly capture ticker identity rather than offering financially meaningful insights specific to each earnings. This is evidenced by two key observations: earnings representations from the same ticker exhibit significantly higher similarity compared to those from different tickers, and predictions from transcript-based models show strong correlations with prior post-earnings volatility.

Same Company, Same Signal: The Role of Identity in Earnings Call Transcripts

The NLP research community has made publicly available numerous instruments for measuring representational harms caused by large language model (LLM)-based systems. These have taken the form of datasets, metrics, and other tools designed to measure harms such as stereotyping and demeaning language. In this study we examine the extent to which such instruments meet the needs of practitioners tasked with evaluating LLM-based systems. Via semi-structured interviews with AI practitioners (N=12), we find that practitioners are often unable to use publicly available instruments for measuring representational harms. We identify two types of challenges. In some cases, instruments are not useful because they do not meaningfully measure what practitioners seek to measure or are otherwise misaligned with practitioner needs. In other cases, instruments - even useful instruments - are not used by practitioners due to institutional barriers impeding their uptake. Drawing on measurement theory and pragmatic measurement, we provide recommendations for addressing these challenges to better meet practitioner needs.

Understanding and Meeting Practitioner Needs When Measuring Representational Harms Caused by LLM-Based Systems

The lack of explainability in state-of-the-art Natural Language Understanding (NLU) classification models has increased interest in developing techniques for improving explainable linear feature-based models (e.g., Logistic Regression/SVM). Politeness detection is a task that exemplifies this interest. While those techniques perform well on the task when applied to data from the same domain as the training data, they lack generalizability and thus fall short when applied to data from other domains. This is due to their reliance on discovering domain-specific word-level features. We introduce a method for improving the generalizability of explainable politeness models by relying on speech act patterns instead of words, leveraging speech act labels assigned by the GPT-4 model. This approach goes beyond the mere words and injects intent into politeness classification models, enhancing their generalizability. Results demonstrate that the proposed method achieves state-of-the-art accuracy in the cross-domain setting among explainable methods, while falling short in the in-domain setting. Our findings illustrate that explainable models can benefit from Large Language Models.

Speech Act Patterns for Improving Generalizability of Explainable Politeness Detection Models

Long-form question answering (LFQA) aims to provide thorough and in-depth answers to complex questions, enhancing comprehension. However, such detailed responses are prone to hallucinations and factual inconsistencies, challenging their faithful evaluation. This work introduces HaluQuestQA, the first hallucination dataset with localized error annotations for human-written and model-generated LFQA answers. HaluQuestQA comprises 698 QA pairs with 1.8k span-level error annotations for five different error types by expert annotators, along with preference judgments. Using our collected data, we thoroughly analyze the shortcomings of long-form answers and find that they lack comprehensiveness and provide unhelpful references. We train an automatic feedback model on this dataset that predicts error spans with incomplete information and provides associated explanations. Finally, we propose a prompt-based approach, Error-Informed Refinement, that uses signals from the learned feedback model to refine generated answers, which we show reduces errors and improves the quality of the answers across multiple models. Furthermore, humans find the answers generated by our approach comprehensive and highly prefer them (84%) over the baseline answers.

Localizing and Mitigating Errors in Long-form Question Answering

Prompt optimization aims to search for effective prompts that enhance the performance of large language models (LLMs). Although existing prompt optimization methods have discovered effective prompts, they often differ from sophisticated prompts carefully designed by human experts. Prompt design strategies, representing best practices for improving prompt performance, can be key to improving prompt optimization. Recently, a method termed the Autonomous Prompt Engineering Toolbox (APET) has incorporated various prompt design strategies into the prompt optimization process. In APET, the LLM is needed to implicitly select and apply the appropriate strategies because prompt design strategies can have negative effects. This implicit selection may be suboptimal due to the limited optimization capabilities of LLMs. This paper introduces Optimizing Prompts with sTrategy Selection (OPTS), which implements explicit selection mechanisms for prompt design. We propose three mechanisms, including a Thompson sampling-based approach, and integrate them into EvoPrompt, a well-known prompt optimizer. Experiments optimizing prompts for two LLMs, Llama-3-8B-Instruct and GPT-4o mini, were conducted using BIG-Bench Hard. Our results show that the selection of prompt design strategies improves the performance of EvoPrompt, and the Thompson sampling-based mechanism achieves the best overall results.

Bandit-Based Prompt Design Strategy Selection Improves Prompt Optimizers

Large Language Models (LLMs) have been evaluated using diverse question types, e.g., multiple-choice, true/false, and short/long answers. This study answers an unexplored question about the impact of different question types on LLM accuracy on reasoning tasks. We investigate the performance of five LLMs on three different types of questions using quantitative and deductive reasoning tasks. The performance metrics include accuracy in the reasoning steps and choosing the final answer. Key Findings: (1) Significant differences exist in LLM performance across different question types. (2) Reasoning accuracy does not necessarily correlate with the final selection accuracy. (3) The number of options and the choice of words, influence LLM performance.

Is Large Language Model Performance on Reasoning Tasks Impacted by Different Ways Questions Are Asked?

Recent advances in Large Language Models(LLMs) have led to remarkable achievements across a variety of Natural Language Processing(NLP) tasks, making prompt engineering increasingly central to guiding model outputs. While manual methods (e.g., “chain-of-thought,” “step-by-step” prompts) can be effective, they typically rely on intuition and do not automatically refine prompts over time. In contrast, automatic prompt optimization employing heuristic-based search algorithms can systematically explore and improve prompts with minimal human oversight. This survey proposes a comprehensive taxonomy of these methods, categorizing them by where optimization occurs, what is optimized, what criteria drive the optimization, which operators generate new prompts, and which iterative search algorithms are applied. We further highlight specialized datasets and tools that support and accelerate automated prompt refinement. We conclude by discussing key open challenges, pointing toward future opportunities for more robust and versatile LLM applications.

Heuristic-based Search Algorithm in Automatic Instruction-focused Prompt Optimization: A Survey

Multi-agent large language model (LLM) systems have shown remarkable performance in tasks such as reasoning, planning, and decision-making. However, their applicability is limited by challenges such as high computational costs and robustness issues. In this work, we identify and systematically evaluate a critical yet overlooked challenge: sycophancy, where agents reinforce each other’s responses instead of critically engaging with the debate. This behavior inflates computational costs by requiring additional debate rounds to reach consensus, limiting the efficiency of multi-agent LLM systems. Through experiments on six benchmark reasoning datasets across three models, we analyze the impact of sycophancy and its role in reducing the reliability of multi-agent debate. Motivated by our findings, we propose CONSENSAGENT, a novel framework that dynamically refines prompts based on agent interactions to mitigate sycophancy. CONSENSAGENT reduces the number of debate rounds required for consensus and improves accuracy while maintaining efficiency. It significantly outperforms both single-agent and multi-agent baselines, achieving state-of-the-art results across all benchmark datasets. Our findings highlight the crucial role of structured prompt optimization in multi-agent setups and establish a foundation for more reliable, efficient multi-agent LLM systems in real-world applications.

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Each graph is a new language: Graph Learning with LLMs

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