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Found 22 papers, 7 papers with code
Think Before You Speak: Learning to Generate Implicit Knowledge for Response Generation by Self-Talk
We further investigate can such models identify when to generate implicit background knowledge and when it is not necessary.
Self-Discover: Large Language Models Self-Compose Reasoning Structures
We introduce SELF-DISCOVER, a general framework for LLMs to self-discover the task-intrinsic reasoning structures to tackle complex reasoning problems that are challenging for typical prompting methods.
AutoMix: Automatically Mixing Language Models
Large language models (LLMs) are now available from cloud API providers in various sizes and configurations.
How FaR Are Large Language Models From Agents with Theory-of-Mind?
We propose a new evaluation paradigm for large language models (LLMs): Thinking for Doing (T4D), which requires models to connect inferences about others' mental states to actions in social scenarios.
SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization
Data scarcity has been a long standing issue in the field of open-domain social dialogue.
I Cast Detect Thoughts: Learning to Converse and Guide with Intents and Theory-of-Mind in Dungeons and Dragons
We propose a novel task, G4C, to study teacher-student natural language interactions in a goal-driven and grounded environment.
Reflect, Not Reflex: Inference-Based Common Ground Improves Dialogue Response Quality
Human communication relies on common ground (CG), the mutual knowledge and beliefs shared by participants, to produce coherent and interesting conversations.
Detecting Political Biases of Named Entities and Hashtags on Twitter
We examine a variety of applications and we thereby demonstrate the effectiveness of our PEM model.
The Role of Facial Expressions and Emotion in ASL
There is little prior work on quantifying the relationships between facial expressions and emotionality in American Sign Language.
An Intelligent Self-driving Truck System For Highway Transportation
Recently, there have been many advances in autonomous driving society, attracting a lot of attention from academia and industry.
Think Before You Speak: Explicitly Generating Implicit Commonsense Knowledge for Response Generation
Implicit knowledge, such as common sense, is key to fluid human conversations.
Commonsense-Focused Dialogues for Response Generation: An Empirical Study
Moreover, existing dialogue datasets do not explicitly focus on exhibiting commonsense as a facet.
An RF-source-free microwave photonic radar with an optically injected semiconductor laser for high-resolution detection and imaging
This paper presents a novel microwave photonic (MWP) radar scheme that is capable of optically generating and processing broadband linear frequency-modulated (LFM) microwave signals without using any radio-frequency (RF) sources.
Incorporating Commonsense Knowledge Graph in Pretrained Models for Social Commonsense Tasks
Pretrained language models have excelled at many NLP tasks recently; however, their social intelligence is still unsatisfactory.
Go Beyond Plain Fine-tuning: Improving Pretrained Models for Social Commonsense
Towards improving language models' social intelligence, we focus on the Social IQA dataset, a task requiring social and emotional commonsense reasoning.
Probing Commonsense Explanation in Dialogue Response Generation
Humans use commonsense reasoning (CSR) implicitly to produce natural and coherent responses in conversations.
Lawyers are Dishonest? Quantifying Representational Harms in Commonsense Knowledge Resources
In addition, we analyze two downstream models that use ConceptNet as a source for commonsense knowledge and find the existence of biases in those models as well.
RICA: Evaluating Robust Inference Capabilities Based on Commonsense Axioms
Pre-trained language models (PTLMs) have achieved impressive performance on commonsense inference benchmarks, but their ability to employ commonsense to make robust inferences, which is crucial for effective communications with humans, is debated.
CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning
In this paper, we present a constrained text generation task, CommonGen associated with a benchmark dataset, to explicitly test machines for the ability of generative commonsense reasoning.
Ranked #1 on
Text Generation
on CommonGen
Retrofitting Contextualized Word Embeddings with Paraphrases
Contextualized word embedding models, such as ELMo, generate meaningful representations of words and their context.
Examining Gender Bias in Languages with Grammatical Gender
Recent studies have shown that word embeddings exhibit gender bias inherited from the training corpora.
Quantification and Analysis of Scientific Language Variation Across Research Fields
Quantifying differences in terminologies from various academic domains has been a longstanding problem yet to be solved.
