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Found 10 papers, 9 papers with code
SCOTT: Self-Consistent Chain-of-Thought Distillation
While CoT can yield dramatically improved performance, such gains are only observed for sufficiently large LMs.
PINTO: Faithful Language Reasoning Using Prompt-Generated Rationales
Neural language models (LMs) have achieved impressive results on various language-based reasoning tasks by utilizing latent knowledge encoded in their own pretrained parameters.
Contextualized Scene Imagination for Generative Commonsense Reasoning
In this paper, we propose an Imagine-and-Verbalize (I&V) method, which learns to imagine a relational scene knowledge graph (SKG) with relations between the input concepts, and leverage the SKG as a constraint when generating a plausible scene description.
Do Language Models Perform Generalizable Commonsense Inference?
Inspired by evidence that pretrained language models (LMs) encode commonsense knowledge, recent work has applied LMs to automatically populate commonsense knowledge graphs (CKGs).
Learning to Deceive Knowledge Graph Augmented Models via Targeted Perturbation
Knowledge graphs (KGs) have helped neural models improve performance on various knowledge-intensive tasks, like question answering and item recommendation.
When Hearst Is not Enough: Improving Hypernymy Detection from Corpus with Distributional Models
We also demonstrate that distributional methods are ideal to make up for pattern-based ones in such cases.
Connecting the Dots: A Knowledgeable Path Generator for Commonsense Question Answering
In this paper, we augment a general commonsense QA framework with a knowledgeable path generator.
Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering
Existing work on augmenting question answering (QA) models with external knowledge (e. g., knowledge graphs) either struggle to model multi-hop relations efficiently, or lack transparency into the model's prediction rationale.
Logic Attention Based Neighborhood Aggregation for Inductive Knowledge Graph Embedding
Recent efforts on this issue suggest training a neighborhood aggregator in conjunction with the conventional entity and relation embeddings, which may help embed new entities inductively via their existing neighbors.
Incorporating GAN for Negative Sampling in Knowledge Representation Learning
In this GAN-based framework, we take advantage of a generator to obtain high-quality negative samples.
