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Found 7 papers, 4 papers with code
Towards Unified Task Embeddings Across Multiple Models: Bridging the Gap for Prompt-Based Large Language Models and Beyond
Task embedding, a meta-learning technique that captures task-specific information, has become prevalent, especially in areas such as multi-task learning, model editing, and interpretability.
Large Language Models Fall Short: Understanding Complex Relationships in Detective Narratives
Existing datasets for narrative understanding often fail to represent the complexity and uncertainty of relationships in real-life social scenarios.
OpenToM: A Comprehensive Benchmark for Evaluating Theory-of-Mind Reasoning Capabilities of Large Language Models
Neural Theory-of-Mind (N-ToM), machine's ability to understand and keep track of the mental states of others, is pivotal in developing socially intelligent agents.
OpenPI2.0: An Improved Dataset for Entity Tracking in Texts
An earlier dataset, OpenPI, provided crowdsourced annotations of entity state changes in text.
Exploring the Curious Case of Code Prompts
Furthermore, we show that the style of code prompt has a large effect on performance for some but not all tasks and that fine-tuning on text instructions leads to better relative performance of code prompts.
Human-in-the-Loop Schema Induction
Schema induction builds a graph representation explaining how events unfold in a scenario.
Causal Reasoning of Entities and Events in Procedural Texts
By injecting the causal relations between entities and events as intermediate reasoning steps in our representation, we further boost the performance to . 67 F1.
