Thailand

Recent research shows that more data and larger models can provide more accurate solutions to natural language problems that require reasoning. However, models can easily fail to provide solutions in unobserved levels of compositional complexity because they might not obtain the level of abstraction needed for generalizability. To alleviate this issue, we propose to train the models with neuro-symbolic techniques that can exploit the logical rules of reasoning as constraints and provide additional supervision sources to the model. Training models to adhere to the regulations of reasoning pushes them to make more effective abstractions needed for generalizability and transfer learning. We focus on a challenging problem of spatial reasoning over text, and our results on multiple benchmarks confirm our hypothesis of effective domain transfer based on neuro-symbolic training. We utilize our symbolic training approach on multiple commonly used language models.

ACL 2024

Tuning Language Models with Spatial Logic for Complex Reasoning

constraint based learning

neuro-symbolic training

spatial question answering

spatial reasoning

### 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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NAACL 2025

Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

workshop paper

### Welcome!
The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. Our Virtual Poster Sessions will take place online Thursday, August 22, 2024.

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The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

In this work we examine the use of Large Language Models (LLMs) in the challenging setting of acting as a Minecraft agent. We apply and evaluate LLMs in the builder and architect settings, introduce clarification questions and examining the challenges and opportunities for improvement. In addition, we present a platform for online interaction with the agents and an evaluation against previous works.

Large Language Models as Minecraft Agents

Building embodied AI systems that can follow arbitrary language instructions in any 3D environment is a key challenge for creating general AI. Accomplishing this goal requires learning to ground language in perception and embodied actions, in order to accomplish complex tasks. The Scalable, Instructable, Multiworld Agent (SIMA) project tackles this by training agents to follow free-form instructions across a diverse range of virtual 3D environments, including curated research environments as well as open-ended, commercial video games. Our goal is to develop an instructable agent that can accomplish anything a human can do in any simulated 3D environment. In this paper we describe our motivation and goal, the initial progress we have made, and promising preliminary results on several diverse research environments and a variety of commercial video games.

Scaling Instructable Agents Across Many Simulated Worlds

The popularity of role-playing video games (RPGs) continues to increase over time. These types of games often afford players multiple options for how they wish to accomplish any given task, which adds to their appeal. Most commonly, RPGs employ a finite state dialogue management system taking the form of a graph structure, where the set of possible utterances at a given node depend on the current game state. From the player's perspective, these appear as a list of pre-written dialogue options that can be chosen to advance the conversation, thus limiting a player's role-playing to the available options. In this paper, we investigate a hybrid dialogue management approach, which allows players more freedom while staying true to the dialogue graph created by the game's designers. Instead of seeing a list of options, players are provided an open-ended text field. A virtual game master (GM) is then tasked with either mapping the player input to one of the dialogue options, or providing in-character feedback to guide players into typing an utterance which the virtual GM can successfully map to an dialogue option. As a preliminary study, we investigate simulated players with a suite of virtual GMs, including a random baseline within the video game Disco Elysium: The Final Cut.

Hybrid Dialogue Management for Task-Oriented Dialogue in Video Games

We present PLUGH a modern benchmark that currently consists of 5 tasks, each with 125 input texts extracted from 48 different games and representing 61 different (non-isomorphic) spatial graphs to assess the abilities of Large Language Models (LLMs) for spatial understanding and reasoning. Our evaluation of API-based and open-sourced LLMs shows that while some commercial LLMs exhibit strong reasoning abilities, open-sourced competitors can demonstrate almost the same level of quality; however, all models still have significant room for improvement. We identify typical reasons for LLM failures and discuss possible ways to deal with them. Datasets and evaluation code are released.

PLUGH: A Benchmark for Spatial Understanding and Reasoning in Large Language Models

In a conversation between two parties, the speaker intends to convey a concept through language while the listener interprets this utterance based on context including their sociocultural background. Differences in culture may lead to differences in interpretations of the meaning of words, resulting in "pragmatic failure": for example, "football" refers to two distinct sports in the United States and in Europe. We seek to understand how language agents may overcome this lack of common ground through the collaborative game Codenames Duet \cite{cross-cultural-codes}, where players have constrained communication and must overcome conceptual and pragmatic misunderstandings to win. By adapting the Rational Speech Acts (RSA) framework \cite{GOODMAN2016818} to model efficient communication and pragmatic reasoning while simulating cross-cultural communication, we introduce a new method RSA+C3 that is able to adapt to players of different cultures without shared common ground. Our contributions towards resolving pragmatic failure in the communicative environment of Codenames Duet are: (1) modeling Codenames players through contrastive learning of an embedding space and LLM prompting, (2) studying cultural differences reflected in our trained models, and (3) demonstrating that our method RSA+C3 can ease cross-cultural communication in gameplay by inferring sociocultural context from interaction.

Communicate to Play: Pragmatic Reasoning for Efficient Cross-Cultural Communication in Codenames

We constructed a database of Japanese expressions based on route information. Using 20 maps as stimuli, we requested descriptions of routes between two points on each map from 40 individuals per route, collecting 1600 route information reference expressions. We determined whether the expressions were based solely on relative reference expressions by using landmarks on the maps. In cases in which only relative reference expressions were used, we labeled the presence or absence of information regarding the starting point, waypoints, and destination. Additionally, we collected clarity ratings for each expression using a survey.

Collection of Japanese Route Information Reference Expressions Using Maps as Stimuli

Top-view perspective denotes a typical way in which humans read and reason over different types of maps, and it is vital for localization and navigation of humans as well as of 'non-human' agents, such as the ones backed by large Vision-Language Models (VLMs). Nonetheless, spatial reasoning capabilities of modern VLMs remain unattested and underexplored. In this work, we thus study their capability to understand and reason over spatial relations from the top view. The focus on top view also enables controlled evaluations at different granularity of spatial reasoning; we clearly disentangle different abilities (e.g., recognizing particular objects versus understanding their relative positions). We introduce the TopViewRS (Top-View Reasoning in Space) dataset, consisting of 11,384 multiple-choice questions with either realistic or semantic top-view map as visual input. We then use it to study and evaluate VLMs across 4 perception and reasoning tasks with different levels of complexity. Evaluation of 10 representative open- and closed-source VLMs reveals the gap of more than 50% compared to average human performance, and it is even lower than the random baseline in some cases. Although additional experiments show that Chain-of-Thought reasoning can boost model capabilities by 5.82% on average, the overall performance of VLMs remains limited. Our findings underscore the critical need for enhanced model capability in top-view spatial reasoning and set a foundation for further research towards human-level proficiency of VLMs in real-world multimodal tasks.

TopViewRS: Vision-Language Models as Top-View Spatial Reasoners

This paper introduces the concept of Language-Guided World Models (LWMs) -- probabilistic models that can simulate environments by reading texts. Agents equipped with these models provide humans with more extensive and efficient control, allowing them to simultaneously alter agent behaviors in multiple tasks via natural verbal communication. In this work, we take initial steps in developing robust LWMs that can generalize to compositionally novel language descriptions. We design a challenging world modeling benchmark based on the game of MESSENGER (Hanjie et al., 2021), featuring evaluation settings that require varying degrees of compositional generalization. Our experiments reveal the lack of generalizability of the state-of-the-art Transformer model, as it offers marginal improvements in simulation quality over a no-text baseline. We devise a more robust model by fusing the Transformer with the EMMA attention mechanism (Hanjie et al., 2021). Our model substantially outperforms the Transformer and approaches the performance of a model with an oracle semantic parsing and grounding capability. To demonstrate the practicality of this model in improving AI safety and transparency, we simulate a scenario in which the model enables an agent to present plans to a human before execution, and to revise plans based on their language feedback.

Language-guided World Models: A Model-based Approach to AI Control

In this work, we evaluate the adaptability of neural agents towards assumed partner behaviors in a collaborative reference game. In this game, success is achieved when a knowledgeable guide can verbally lead a follower to the selection of a specific puzzle piece among several distractors. We frame this language grounding and coordination task as a reinforcement learning problem and measure to which extent a common reinforcement training algorithm (PPO) is able to produce neural agents (the guides) that perform well with various heuristic follower behaviors that vary along the dimensions of confidence and autonomy. We experiment with a learning signal that in addition to the goal condition also respects an assumed communicative effort. Our results indicate that this novel ingredient leads to communicative strategies that are less verbose (staying silent in some of the steps) and that with respect to that the guide’s strategies indeed adapt to the partner’s level of confidence and autonomy.

Learning Communication Policies for Different Follower Behaviors in a Collaborative Reference Game

We create MTI(Multi-Task Inference)-Bench and test whether language models can solve complicated instructions, consisted of two or more instructions, at once.

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