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Found 6 papers, 5 papers with code
Towards Uncertainty-Aware Language Agent
We present the Uncertainty-Aware Language Agent (UALA), a framework that orchestrates the interaction between the agent and the external world using uncertainty quantification.
POSQA: Probe the World Models of LLMs with Size Comparisons
Embodied language comprehension emphasizes that language understanding is not solely a matter of mental processing in the brain but also involves interactions with the physical and social environment.
Reward Engineering for Generating Semi-structured Explanation
Semi-structured explanation depicts the implicit process of a reasoner with an explicit representation.
PiVe: Prompting with Iterative Verification Improving Graph-based Generative Capability of LLMs
We show how a small language model could be trained to act as a verifier module for the output of an LLM(i. e., ChatGPT, GPT-4), and to iteratively improve its performance via fine-grained corrective instructions.
Self-supervised Graph Masking Pre-training for Graph-to-Text Generation
Large-scale pre-trained language models (PLMs) have advanced Graph-to-Text (G2T) generation by processing the linearised version of a graph.
Generating Diverse Descriptions from Semantic Graphs
Text generation from semantic graphs is traditionally performed with deterministic methods, which generate a unique description given an input graph.
