Ruixuan Deng, Xiaoyang Hu, Miles Gilberti, Shane Storks, Aman Taxali, Mike Angstadt, Chandra Sripada, Joyce Chai. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Emergent communication (EmCom) with deep neural network-based agents promises to yield insights into the nature of human language, but remains focused primarily on a few subfieldspecific goals and metrics that prioritize communication schemes which represent attributes with unique characters one-to-one and compose them syntactically.We thus reinterpret a common EmCom setting, the attribute-value reconstruction game, by imposing a smallvocabulary constraint to simulate double articulation, and formulating a novel setting analogous to naturalistic inflectional morphology (enabling meaningful comparison to natural language communication schemes).We develop new metrics and explore variations of this game motivated by real properties of inflectional morphology: concatenativity and fusion.Through our experiments, we discover that simulated phonological constraints encourage concatenative morphology, and emergent languages replicate the tendency of natural languages to fuse grammatical attributes.

Humans acquire syntactic constructions like filler-gap dependencies from limited and often noisy input.Can neural language models do the same?We investigate this question by evaluating GPT-2 models trained on childoriented input from the BabyLM Challenge.Our experiments focus on whether these "baby" language models acquire filler-gap dependencies, generalize across constructions, and respect structural constraints such as island effects.We apply a suite of syntactic constructions to four models trained on child language, including two base models (trained on 10M and 100M tokens) and two well-performing models from the BabyLM Challenge (ConcreteGPT and BabbleGPT).We evaluate model behavior using wh-licensing scores, flip tests, and grammaticality contrasts across four constructions.Results show that BabyLM-scale models partially acquire filler-gap dependencies but often fail to generalize or fully capture island constraints.Our code and datasets are available at https://github.com/um-cap-lab/ EMNLP-2025-submission.

Procedural mistake detection (PMD) is a challenging problem of classifying whether a human user (observed through egocentric video) has successfully executed a task (specified by a procedural text).Despite significant recent efforts, machine performance in the wild remains nonviable, and the reasoning processes underlying this performance are opaque.As such, we extend PMD to require generating visual self-dialog rationales to inform decisions.Given the impressive, mature image understanding capabilities observed in recent visionand-language models (VLMs), we curate a suitable benchmark dataset for PMD based on individual frames.As our reformulation enables unprecedented transparency, we leverage a natural language inference (NLI) model to formulate two automated metrics for the coherence of generated rationales.We establish baselines for this reframed task, showing that VLMs struggle off-the-shelf, but with some trade-offs, their accuracy, coherence, and efficiency can be improved by incorporating these metrics into common inference and finetuning methods.Lastly, our multi-faceted metrics visualize common outcomes, highlighting areas for further improvement.

A major reason behind the recent success of large language models (LLMs) is their incontext learning capability, which makes it possible to rapidly adapt them to downstream textbased tasks by prompting them with a small number of relevant demonstrations.While large vision-language models (VLMs) have recently been developed for tasks requiring both text and images, they largely lack in-context learning over visual information, especially in understanding and generating text about videos.In this work, we implement Emergent In-context Learning on Videos (EILeV), a novel training paradigm that induces in-context learning over video and text by capturing key properties of pre-training data found by prior work to be essential for in-context learning in transformers.In our experiments, we show that EILeV-trained models outperform other off-the-shelf VLMs in few-shot video narration for novel, rare actions.Furthermore, we demonstrate that these key properties of bursty distributions, skewed marginal distributions, and dynamic meaning each contribute to varying degrees to VLMs' in-context learning capability in narrating procedural videos.Our results, analysis, and EILeV-trained models yield numerous insights about the emergence of in-context learning over video and text, creating a foundation for future work to optimize and scale VLMs for open-domain video understanding and reasoning.1

Analogical reasoning is a fundamental capacity of human cognition that allows us to reason abstractly about novel situations by relating them to past experiences.While it is thought to be essential for robust reasoning in AI systems, conventional approaches require significant training and/or hard-coding of domain knowledge to be applied to benchmark tasks.Inspired by cognitive science research that has found connections between human language and analogy-making, we explore the use of intuitive language-based abstractions to support analogy in AI systems.Specifically, we apply large pre-trained language models (PLMs) to visual Raven's Progressive Matrices (RPM), a common relational reasoning test.By simply encoding the perceptual features of the problem into language form, we find that PLMs exhibit a striking capacity for zero-shot relational reasoning, exceeding human performance and nearing supervised vision-based methods.We explore different encodings that vary the level of abstraction over task features, finding that higherlevel abstractions further strengthen PLMs' analogical reasoning.Our detailed analysis reveals insights on the role of model complexity, incontext learning, and prior knowledge in solving RPM tasks.

As natural language processing (NLP) has recently seen an unprecedented level of excitement, and more people are eager to enter the field, it is unclear whether current research reproducibility efforts are sufficient for this group of beginners to apply the latest developments.To understand their needs, we conducted a study with 93 students in an introductory NLP course, where students reproduced the results of recent NLP papers.Surprisingly, we find that their programming skill and comprehension of research papers have a limited impact on their effort spent completing the exercise.Instead, we find accessibility efforts by research authors to be the key to success, including complete documentation, better coding practice, and easier access to data files.Going forward, we recommend that NLP researchers pay close attention to these simple aspects of open-sourcing their work, and use insights from beginners' feedback to provide actionable ideas on how to better support them.

Pre-trained language models (PLMs) have shown impressive performance in various language tasks.However, they are prone to spurious correlations, and often generate illusory information.In real-world applications, PLMs should justify decisions with formalized, coherent reasoning chains, but this challenge remains under-explored.Cognitive psychology theorizes that humans are capable of utilizing fast and intuitive heuristic thinking to make decisions based on past experience, then rationalizing the decisions through slower and deliberative analytic reasoning.We incorporate these interlinked dual processes in fine-tuning and in-context learning with PLMs, applying them to two language understanding tasks that require coherent physical commonsense reasoning.We show that our proposed Heuristic-Analytic Reasoning (HAR) strategies drastically improve the coherence of rationalizations for model decisions, yielding state-of-the-art results on Tiered Reasoning for Intuitive Physics (TRIP).We also find that this improved coherence is a direct result of more faithful attention to relevant language context in each step of reasoning.Our findings suggest that human-like reasoning strategies can effectively improve the coherence and reliability of PLM reasoning.

Yichi Zhang, Jianing Yang, Jiayi Pan, Shane Storks, Nikhil Devraj, Ziqiao Ma, Keunwoo Yu, Yuwei Bao, Joyce Chai. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 2022.