Search Results for author:
Found 42 papers, 14 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.
Numeracy enhances the Literacy of Language Models
This paper studies the effect of using six different number encoders on the task of masked word prediction (MWP), as a proxy for evaluating literacy.
MARVEL: Multidimensional Abstraction and Reasoning through Visual Evaluation and Learning
Further analysis of perception questions reveals that MLLMs struggle to comprehend the visual features (near-random performance) and even count the panels in the puzzle ( <45%), hindering their ability for abstract reasoning.
Knowledge-Powered Recommendation for an Improved Diet Water Footprint
According to WWF, 1. 1 billion people lack access to water, and 2. 7 billion experience water scarcity at least one month a year.
Integrating Pre-Trained Language Model with Physical Layer Communications
The burgeoning field of on-device AI communication, where devices exchange information directly through embedded foundation models, such as language models (LMs), requires robust, efficient, and generalizable communication frameworks.
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.
The Curious Case of Nonverbal Abstract Reasoning with Multi-Modal Large Language Models
While large language models (LLMs) are still being adopted to new domains and utilized in novel applications, we are experiencing an influx of the new generation of foundation models, namely multi-modal large language models (MLLMs).
Faithful Persona-based Conversational Dataset Generation with Large Language Models
Training Natural Language Processing (NLP) models on a diverse and comprehensive persona-based dataset can lead to conversational models that create a deeper connection with the user, and maintain their engagement.
Making Large Language Models Better Data Creators
In our experiments we demonstrate that instruction-following LLMs are highly cost-effective data creators, and that models trained with these data exhibit performance better than those trained with human-labeled data (by up to 17. 5%) on out-of-distribution evaluation, while maintaining comparable performance on in-distribution tasks.
Ranked #3 on
Visual Question Answering
on ViP-Bench
Learn Your Tokens: Word-Pooled Tokenization for Language Modeling
Language models typically tokenize text into subwords, using a deterministic, hand-engineered heuristic of combining characters into longer surface-level strings such as 'ing' or whole words.
Estimating Numbers without Regression
Despite recent successes in language models, their ability to represent numbers is insufficient.
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.
Finding Pragmatic Differences Between Disciplines
Using a corpus of scholarly documents across 19 disciplines and state-of-the-art language modeling techniques, we learn a fixed set of domain-agnostic descriptors for document sections and "retrofit" the corpus to these descriptors (also referred to as "normalization").
Analyzing Norm Violations in Live-Stream Chat
Previous approaches to detecting toxic language and norm violations have been primarily concerned with conversations from online forums and social media, such as Reddit and Twitter.
Temporal Knowledge Graph Forecasting Without Knowledge Using In-Context Learning
This shows that prior semantic knowledge is unnecessary; instead, LLMs can leverage the existing patterns in the context to achieve such performance.
PubGraph: A Large-Scale Scientific Knowledge Graph
In this paper, we present PubGraph, a new resource for studying scientific progress that takes the form of a large-scale knowledge graph (KG) with more than 385M entities, 13B main edges, and 1. 5B qualifier edges.
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.
XMD: An End-to-End Framework for Interactive Explanation-Based Debugging of NLP Models
Explanation-based model debugging aims to resolve spurious biases by showing human users explanations of model behavior, asking users to give feedback on the behavior, then using the feedback to update the model.
Seq2Seq-SC: End-to-End Semantic Communication Systems with Pre-trained Language Model
In this work, we propose a realistic semantic network called seq2seq-SC, designed to be compatible with 5G NR and capable of working with generalized text datasets using a pre-trained language model.
Does Wikidata Support Analogical Reasoning?
Analogical reasoning methods have been built over various resources, including commonsense knowledge bases, lexical resources, language models, or their combination.
Understanding Narratives through Dimensions of Analogy
Experiments with language models and neuro-symbolic AI reasoners on these tasks reveal that state-of-the-art methods can be applied to reason by analogy with a limited success, motivating the need for further research towards comprehensive and scalable analogical reasoning by AI.
FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue
Task transfer, transferring knowledge contained in related tasks, holds the promise of reducing the quantity of labeled data required to fine-tune language models.
Leveraging Visual Knowledge in Language Tasks: An Empirical Study on Intermediate Pre-training for Cross-modal Knowledge Transfer
Pre-trained language models are still far from human performance in tasks that need understanding of properties (e. g. appearance, measurable quantity) and affordances of everyday objects in the real world since the text lacks such information due to reporting bias.
Evaluating Machine Common Sense via Cloze Testing
Language models (LMs) show state of the art performance for common sense (CS) question answering, but whether this ability implies a human-level mastery of CS remains an open question.
Think Before You Speak: Explicitly Generating Implicit Commonsense Knowledge for Response Generation
Implicit knowledge, such as common sense, is key to fluid human conversations.
Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER
We also find that good demonstration can save many labeled examples and consistency in demonstration contributes to better performance.
Commonsense-Focused Dialogues for Response Generation: An Empirical Study
Moreover, existing dialogue datasets do not explicitly focus on exhibiting commonsense as a facet.
AutoTriggER: Label-Efficient and Robust Named Entity Recognition with Auxiliary Trigger Extraction
Deep neural models for named entity recognition (NER) have shown impressive results in overcoming label scarcity and generalizing to unseen entities by leveraging distant supervision and auxiliary information such as explanations.
Low Resource Named Entity Recognition
named-entity-recognition
+2
Table-based Fact Verification with Salience-aware Learning
From one perspective, our system conducts masked salient token prediction to enhance the model for alignment and reasoning between the table and the statement.
Ranked #8 on
Table-based Fact Verification
on TabFact
Story Generation with Commonsense Knowledge Graphs and Axioms
Our method aligns story types with commonsense axioms, and queries to a commonsense knowledge graph, enabling the generation of hundreds of thousands of stories.
Retrieving Complex Tables with Multi-Granular Graph Representation Learning
The task of natural language table retrieval (NLTR) seeks to retrieve semantically relevant tables based on natural language queries.
Probing Commonsense Explanation in Dialogue Response Generation
Humans use commonsense reasoning (CSR) implicitly to produce natural and coherent responses in conversations.
Representing Numbers in NLP: a Survey and a Vision
NLP systems rarely give special consideration to numbers found in text.
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.
Human-like Time Series Summaries via Trend Utility Estimation
Our system finds patterns in time series data and ranks these patterns based on empirical observations of human behavior using utility estimation.
Using Noisy Extractions to Discover Causal Knowledge
Knowledge bases (KB) constructed through information extraction from text play an important role in query answering and reasoning.
Sparsity and Noise: Where Knowledge Graph Embeddings Fall Short
Knowledge graph (KG) embedding techniques use structured relationships between entities to learn low-dimensional representations of entities and relations.
Generic Statistical Relational Entity Resolution in Knowledge Graphs
A common theme in this research has been the importance of incorporating relational features into the resolution process.
Adaptive Neighborhood Graph Construction for Inference in Multi-Relational Networks
A neighborhood graph, which represents the instances as vertices and their relations as weighted edges, is the basis of many semi-supervised and relational models for node labeling and link prediction.
