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Found 12 papers, 9 papers with code
Eliciting In-Context Learning in Vision-Language Models for Videos Through Curated Data Distributional Properties
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.
Can Foundation Models Watch, Talk and Guide You Step by Step to Make a Cake?
Despite tremendous advances in AI, it remains a significant challenge to develop interactive task guidance systems that can offer situated, personalized guidance and assist humans in various tasks.
From Heuristic to Analytic: Cognitively Motivated Strategies for Coherent Physical Commonsense 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.
In-Context Analogical Reasoning with Pre-Trained Language Models
Analogical reasoning is a fundamental capacity of human cognition that allows us to reason abstractly about novel situations by relating them to past experiences.
NLP Reproducibility For All: Understanding Experiences of Beginners
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.
DANLI: Deliberative Agent for Following Natural Language Instructions
These reactive agents are insufficient for long-horizon complex tasks.
Reproducibility Beyond the Research Community: Experience from NLP Beginners
As NLP research attracts public attention and excitement, it becomes increasingly important for it to be accessible to a broad audience.
Best of Both Worlds: A Hybrid Approach for Multi-Hop Explanation with Declarative Facts
Language-enabled AI systems can answer complex, multi-hop questions to high accuracy, but supporting answers with evidence is a more challenging task which is important for the transparency and trustworthiness to users.
Tiered Reasoning for Intuitive Physics: Toward Verifiable Commonsense Language Understanding
However, evaluations only based on end task performance shed little light on machines' true ability in language understanding and reasoning.
Beyond the Tip of the Iceberg: Assessing Coherence of Text Classifiers
As large-scale, pre-trained language models achieve human-level and superhuman accuracy on existing language understanding tasks, statistical bias in benchmark data and probing studies have recently called into question their true capabilities.
Are We There Yet? Learning to Localize in Embodied Instruction Following
Embodied instruction following is a challenging problem requiring an agent to infer a sequence of primitive actions to achieve a goal environment state from complex language and visual inputs.
Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches
In the NLP community, recent years have seen a surge of research activities that address machines' ability to perform deep language understanding which goes beyond what is explicitly stated in text, rather relying on reasoning and knowledge of the world.
