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Found 13 papers, 2 papers with code
An End-to-end Neural Natural Language Interface for Databases
The ability to extract insights from new data sets is critical for decision making.
Probing Neural Language Models for Human Tacit Assumptions
Our prompts are based on human responses in a psychological study of conceptual associations.
COD3S: Diverse Generation with Discrete Semantic Signatures
We present COD3S, a novel method for generating semantically diverse sentences using neural sequence-to-sequence (seq2seq) models.
InFillmore: Frame-Guided Language Generation with Bidirectional Context
We propose a structured extension to bidirectional-context conditional language generation, or "infilling," inspired by Frame Semantic theory (Fillmore, 1976).
One-Shot Learning from a Demonstration with Hierarchical Latent Language
Humans have the capability, aided by the expressive compositionality of their language, to learn quickly by demonstration.
NELLIE: A Neuro-Symbolic Inference Engine for Grounded, Compositional, and Explainable Reasoning
Our goal is a modern approach to answering questions via systematic reasoning where answers are supported by human interpretable proof trees grounded in an NL corpus of authoritative facts.
Defending Against Disinformation Attacks in Open-Domain Question Answering
Recent work in open-domain question answering (ODQA) has shown that adversarial poisoning of the search collection can cause large drops in accuracy for production systems.
Ontologically Faithful Generation of Non-Player Character Dialogues
We introduce a language generation task grounded in a popular video game environment.
"According to ...": Prompting Language Models Improves Quoting from Pre-Training Data
Large Language Models (LLMs) may hallucinate and generate fake information, despite pre-training on factual data.
Reframing Tax Law Entailment as Analogical Reasoning
We re-frame statutory reasoning as an analogy task, where each instance of the analogy task involves a combination of two instances of statutory reasoning.
Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic
Contemporary language models enable new opportunities for structured reasoning with text, such as the construction and evaluation of intuitive, proof-like textual entailment trees without relying on brittle formal logic.
TV-TREES: Multimodal Entailment Trees for Neuro-Symbolic Video Reasoning
It is challenging to perform question-answering over complex, multimodal content such as television clips.
SELF-[IN]CORRECT: LLMs Struggle with Refining Self-Generated Responses
Can LLMs continually improve their previous outputs for better results?
