United States

Faithfulness evaluators based on Large Language Models (LLMs) are often fooled by the fluency of the text and struggle with identifying errors in the summaries, usually leading to high false negative rate. We propose an approach to summary faithfulness evaluation in which multiple LLM-based agents are assigned initial stances (regardless of what their belief might be) and forced to come up with a reason to justify the imposed belief, thus engaging in a multi-round debate to reach an agreement. The uniformly distributed initial assignments here result in a greater diversity of stances leading to more meaningful debates and ultimately more errors identified. Furthermore, by analyzing the recent faithfulness evaluation datasets, we observe that naturally, it is not always the case for a summary to be either faithful to the source document or not. We therefore introduce a new dimension ambiguity and a detailed taxonomy to identify such special cases. Experiments demonstrate our approach can help identify ambiguities, and have even a stronger performance on non-ambiguous summaries.

NAACL 2025

Faithful, Unfaithful or Ambiguous? Multi-Agent Debate with Initial Stance for Summary Evaluation

ambiguity

multi-agent

faithfulness

### Welcome to the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL) 
Continuing its mission of expanding and involving the science community of all European countries, EACL had selected the Ukrainian community for the 16th EACL, which took place online due to the COVID pandemic. Unfortunately, the ongoing war made the organisation in Kyiv impossible. Considering the importance of physical interaction among researchers, especially after the restrictions imposed by the COVID pandemic, in addition to an online mode, the conference will be held in Dubrovnik, Croatia, from 2 to 6 of May, 2023. The original aim of strengthening the connection with the Ukrainian community will not change as our program will feature a dedicated session and a workshop to highlight work on Ukrainian language technologies. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

You need to register for the conference in order to access this site. Please visit https://2023.eacl.org/registration for more information.

EACL 2023

As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

technical 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

Conversational recommender systems (CRSs) are designed to suggest the target item that the user is likely to prefer through multi-turn conversations. Recent studies stress that capturing sentiments in user conversations improves recommendation accuracy. However, they employ a single user representation, which may fail to distinguish between contrasting user intentions, such as likes and dislikes, potentially leading to suboptimal performance. To this end, we propose a novel conversational recommender model, called COntrasting user pReference expAnsion and Learning (CORAL). Firstly, CORAL extracts the user’s hidden pref- erences through contrasting preference expansion using the reasoning capacity of the LLMs. Based on the potential preference, CORAL explicitly differentiates the contrasting preferences and leverages them into the recommendation process via preference-aware learning. Extensive experiments show that CORAL significantly outperforms existing methods in three benchmark datasets, improving up to 99.72% in Recall@10. The code and datasets are available at https://github.com/kookeej/CORAL.

Empowering Retrieval-based Conversational Recommendation with Contrasting User Preferences

Appraisal theories suggest that emotions arise from subjective evaluations of events, referred to as appraisals. The taxonomy of appraisals is quite diverse, and they are usually given ratings on a Likert scale to be annotated in an experiencer-annotator or reader-annotator paradigm. This paper studies GPT-4 as a reader-annotator of 21 specific appraisal ratings in different prompt settings, aiming to evaluate and improve its performance compared to human annotators. We found that GPT-4 is an effective reader-annotator that performs close to or even slightly better than human annotators, and its results can be significantly improved by using a majority voting of five completions. GPT-4 also effectively predicts appraisal ratings and emotion labels using a single prompt, but adding instruction complexity results in poorer performance. We also found that longer event descriptions lead to more accurate annotations for both model and human annotator ratings. This work contributes to the growing usage of LLMs in psychology and the strategies for improving GPT-4 performance in annotating appraisals.

Assessing the Reliability and Validity of GPT-4 in Annotating Emotion Appraisal Ratings

Scaling Intelligence the Human Way

The State and Future of SIGMORPHON

Human cognition constrains how we communicate. Our cognitive biases and preferences interact with the processes that drive language emergence and change in non-trivial ways. A powerful method to discern the roles of cognitive biases and processes like language learning and use in shaping linguistic structure is to build agent-based models. Recent advances in computational linguistics and deep learning sparked a renewed interest in such simulations, creating the opportunity to model increasingly realistic phenomena. These models simulate emergent communication, referring to the spontaneous development of a communication system through repeated interactions between individual neural network agents. However, a crucial challenge in this line of work is that such artificial learners still often behave differently from human learners. Directly inspired by human artificial language learning studies, we proposed a novel framework for simulating language learning and change, which allows agents to first learn an artificial language and then use it to communicate, with the aim of studying the emergence of specific linguistic properties. I will show how we used it to simulate the emergence of a well-known language phenomenon: the word-order/case-marking trade-off. I will also share some recent findings where we test for the presence of a well-known human cross-modal mapping preference (the bouba-kiki effect) in vision-and-language models. Cross-modal associations play an essential role in human language understanding, learning, and evolution, but our findings reveal that multimodal language models do not align well with such human preferences. Finally, I will provide a sneak peek at new findings that reveal what a novel artificial language looks like when it has emerged to adapt to preferences of both humans and LLMs in a hybrid language game experiment.

The emergence of language universals in neural agents and vision-and-language models

Transformers has redefined deep learning research and has become the most prominent architecture across domains such as natural language processing, computer vision, and image processing. Attention mechanism, particularly self-attention, is central to the success of this architecture, which allows the model to capture dependencies across the input sequences. However, the fundamental challenge in understanding self-attention is its intrinsic symmetry. The existing works often consider self-attention as a kernel method, leveraging symmetric kernels based on Mercer’s theorem. However, the self-attention matrices used in the transformer architectures are inherently asymmetric, which leads to an inconsistency
between the theoretical formulation and the practical implementation. The primal-attention, a novel attention mechanism based on kernel singular value decomposition explicitly models the asymmetry.
Therefore, reformulating self-attention using primal-dual representation ensures efficient computation and low-rank approximation that enhances performance and generalization.

Understanding Attention in Asymmetric Kernel Point of View

The past and future of grapheme-to-phoneme conversion

Scientific research is inherently slow, complex, and highly specialized, relying on an iterative cycle of hypothesis generation and validation. Can we create an AI-powered biologist to automate these processes at scale? In this talk, I will outline the key ingredients for building such a system. I will first introduce Biomni, a generalist AI agent that integrates large language models with a vast ecosystem of specialized biological tools, software, and databases. By linking AI-driven reasoning with real-world execution, Biomni enables a broad range of biomedical applications. Building on this foundation, I will introduce BioDiscoveryAgent, an AI system for automated hypothesis generation that optimizes experimental design by identifying the most promising perturbations for screening. By leveraging literature-based reasoning, it significantly outperforms traditional Bayesian optimization approaches. Finally, beyond hypothesis generation, rigorous validation is essential. I will present POPPER, an AI-driven sequential falsification framework that automates hypothesis validation with statistical guarantees, ensuring scientific robustness at scale.

AI Agents for Accelerating Scientific Discovery: From Hypothesis Generation to Experimental Design

TxAgent: An AI Agent for Therapeutic Reasoning Across a Universe of Tools

There exist approximately 166 billion small molecules, with 970 million deemed druglike. Similarly there is a vast pool of molecule candidates for new materials. This scarcity underscores the urgent need for innovative approaches, calling upon the NLP community to contribute significantly to medicine and material science. However, the challenges are manifold. Existing large language models (LLMs) alone are insufficient due to their tendency to generate erroneous claims confidently. Moreover, traditional knowledge bases do not adequately address the issue. This gap persists because chemistry language diverges significantly from natural language, demanding specialized domain knowledge, joint molecule and language modeling, and critical thinking. Using drug discovery, personalized drug synergy, and material discovery as case studies, I will present our approaches to tackle these challenges and turn an AI agent into a Medicinal Chemist or Material Scientist. I will share preliminary results from animal testing conducted on drug variants, and newly discovered material variants for efficient Organic Photovoltaic Devices proposed by AI algorithms.

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Empowering Retrieval-based Conversational Recommendation with Contrasting User Preferences

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