Suhaib Abdurahman, Etsuko Ishii, Katerina Margatina, Divya Bhargavi, Monica Sunkara, Yi Zhang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Samuel Cahyawijaya, Delong Chen, Yejin Bang, Leila Khalatbari, Bryan Wilie, Ziwei Ji, Etsuko Ishii, Pascale Fung. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

The capability to reason from text is crucial for real-world NLP applications.Real-world scenarios often involve incomplete or evolving data.In response, individuals update their beliefs and understandings accordingly.However, most existing evaluations assume that language models (LMs) operate with consistent information.We introduce Belief-R 1 , a new dataset designed to test LMs' belief revision ability when presented with new evidence.Inspired by how humans suppress prior inferences, this task assesses LMs within the newly proposed delta reasoning (∆R) framework.Belief-R features sequences of premises designed to simulate scenarios where additional information could necessitate prior conclusions drawn by LMs.We evaluate ∼30 LMs across diverse prompting strategies and found that LMs generally struggle to appropriately revise their beliefs in response to new information.Further, models adept at updating often underperformed in scenarios without necessary updates, highlighting a critical trade-off.These insights underscore the importance of improving LMs' adaptiveness to changing information, a step toward more reliable AI systems.

Inference, especially those derived from inductive processes, is a crucial component in our conversation to complement the information implicitly or explicitly conveyed by a speaker.While recent large language models show remarkable advances in inference tasks, their performance in inductive reasoning, where not all information is present in the context, is far behind deductive reasoning.In this paper, we analyze the behavior of the models based on the task difficulty defined by the semantic information gap -which distinguishes inductive and deductive reasoning (Johnson-Laird, 1988, 1993).Our analysis reveals that the disparity in information between dialogue contexts and desired inferences poses a significant challenge to the inductive inference process.To mitigate this information gap, we investigate a contrastive learning approach by feeding negative samples.Our experiments suggest negative samples help models understand what is wrong and improve their inference generations. 1

Politically sensitive topics are still a challenge for open-domain chatbots.However, dealing with politically sensitive content in a responsible, non-partisan, and safe behavior way is integral for these chatbots.Currently, the main approach to handling political sensitivity is by simply changing such a topic when it is detected.This is safe but evasive and results in a chatbot that is less engaging.In this work, as a first step towards a politically safe chatbot, we propose a group of metrics for assessing their political prudence.We then conduct political prudence analysis of various chatbots and discuss their behavior from multiple angles through our automatic metric and human evaluation metrics.The testsets and codebase are released to promote research in this area.1

Over the past year, research in various domains, including Natural Language Processing (NLP), has been accelerated to fight against the COVID-19 pandemic, yet such research has just started on dialogue systems.In this paper, we introduce an end-to-end dialogue system which aims to ease the isolation of people under self-quarantine.We conduct a control simulation experiment to assess the effects of the user interface, a web-based virtual agent called Nora vs. the android ERICA via a video call.The experimental results show that the android offers a more valuable user experience by giving the impression of being more empathetic and engaging in the conversation due to its nonverbal information, such as facial expressions and body gestures.Demo video available at https://youtu.be/PLPEBXLeKJI.

Conventional multimodal tasks, such as caption generation and visual question answering, have allowed machines to understand an image by describing or being asked about it in natural language, often via a sentence. Datasets for these tasks contain a large number of pairs of an image and the corresponding sentence as an instance. However, a real multimodal document such as a news article or Wikipedia page consists of multiple sentences with multiple images. Such documents require an advanced skill of jointly considering the multiple texts and multiple images, beyond a single sentence and image, for the interpretation. Therefore, aiming at building a system that can understand multimodal documents, we propose a task called image position prediction (IPP). In this task, a system learns plausible positions of images in a given document. To study this task, we automatically constructed a dataset of 66K multimodal documents with 320K images from Wikipedia articles. We conducted a preliminary experiment to evaluate the performance of a current multimodal system on our task. The experimental results show that the system outperformed simple baselines while the performance is still far from human performance, which thus poses new challenges in multimodal research.