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Found 34 papers, 12 papers with code
Compositional Generalization for Kinship Prediction through Data Augmentation
However, predicting and using intermediate kinship graphs leads to a deterioration in the generalization of kinship prediction by around 50% on average relative to models that only leverage data augmentation.
Predicting Difficulty and Discrimination of Natural Language Questions
Item Response Theory (IRT) has been extensively used to numerically characterize question difficulty and discrimination for human subjects in domains including cognitive psychology and education (Primi et al., 2014; Downing, 2003).
Does Social Pressure Drive Persuasion in Online Fora?
Online forums such as ChangeMyView have been explored to research aspects of persuasion and argumentative quality in language.
Fuse to Forget: Bias Reduction and Selective Memorization through Model Fusion
Model fusion research aims to aggregate the knowledge of multiple models to enhance performance by combining their weights.
Leveraging Multiple Teachers for Test-Time Adaptation of Language-Guided Classifiers
Recent approaches have explored language-guided classifiers capable of classifying examples from novel tasks when provided with task-specific natural language explanations, instructions or prompts (Sanh et al., 2022; R. Menon et al., 2022).
A Comparison of Lexicon-Based and ML-Based Sentiment Analysis: Are There Outlier Words?
Our findings are that the importance of a word depends on the domain and there are no standout lexical entries which systematically cause differences in sentiment scores.
Pragmatic Reasoning Unlocks Quantifier Semantics for Foundation Models
Generalized quantifiers (e. g., few, most) are used to indicate the proportions predicates are satisfied (for example, some apples are red).
MaNtLE: Model-agnostic Natural Language Explainer
Understanding the internal reasoning behind the predictions of machine learning systems is increasingly vital, given their rising adoption and acceptance.
Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture
Our AL architecture leverages an explanation-generation model to produce explanations guided by human explanations, a prediction model that utilizes generated explanations toward prediction faithfully, and a novel data diversity-based AL sampling strategy that benefits from the explanation annotations.
Identifying and Manipulating the Personality Traits of Language Models
Psychology research has long explored aspects of human personality such as extroversion, agreeableness and emotional stability.
LaSQuE: Improved Zero-Shot Classification from Explanations Through Quantifier Modeling and Curriculum Learning
A hallmark of human intelligence is the ability to learn new concepts purely from language.
What do Large Language Models Learn beyond Language?
Large language models (LMs) have rapidly become a mainstay in Natural Language Processing.
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
CLUES: A Benchmark for Learning Classifiers using Natural Language Explanations
For this, we introduce CLUES, a benchmark for Classifier Learning Using natural language ExplanationS, consisting of a range of classification tasks over structured data along with natural language supervision in the form of explanations.
Mapping Language to Programs using Multiple Reward Components with Inverse Reinforcement Learning
On the VirtualHome framework, we get improvements of up to 9. 0% on the Longest Common Subsequence metric and 14. 7% on recall-based metrics over previous work on this framework (Puig et al., 2018).
Adversarial Scrubbing of Demographic Information for Text Classification
Contextual representations learned by language models can often encode undesirable attributes, like demographic associations of the users, while being trained for an unrelated target task.
ePiC: Employing Proverbs in Context as a Benchmark for Abstract Language Understanding
While large language models have shown exciting progress on several NLP benchmarks, evaluating their ability for complex analogical reasoning remains under-explored.
How Helpful is Inverse Reinforcement Learning for Table-to-Text Generation?
Many approaches to this problem use Reinforcement Learning (RL), which maximizes a single manually defined reward, such as BLEU.
Improving and Simplifying Pattern Exploiting Training
However, PET uses task-specific unlabeled data.
PRover: Proof Generation for Interpretable Reasoning over Rules
First, PROVER generates proofs with an accuracy of 87%, while retaining or improving performance on the QA task, compared to RuleTakers (up to 6% improvement on zero-shot evaluation).
Learning Web-based Procedures by Reasoning over Explanations and Demonstrations in Context
We explore learning web-based tasks from a human teacher through natural language explanations and a single demonstration.
Learning to Ask for Conversational Machine Learning
Natural language has recently been explored as a new medium of supervision for training machine learning models.
LIA: A Natural Language Programmable Personal Assistant
We present LIA, an intelligent personal assistant that can be programmed using natural language.
A Spatial Model for Extracting and Visualizing Latent Discourse Structure in Text
We present a generative probabilistic model of documents as sequences of sentences, and show that inference in it can lead to extraction of long-range latent discourse structure from a collection of documents.
Zero-shot Learning of Classifiers from Natural Language Quantification
Humans can efficiently learn new concepts using language.
Where Have I Heard This Story Before? Identifying Narrative Similarity in Movie Remakes
People can identify correspondences between narratives in everyday life.
Joint Concept Learning and Semantic Parsing from Natural Language Explanations
Natural language constitutes a predominant medium for much of human learning and pedagogy.
Inferring Interpersonal Relations in Narrative Summaries
In this work, we address the problem of inferring the polarity of relationships between people in narrative summaries.
Modeling Dynamic Relationships Between Characters in Literary Novels
Studying characters plays a vital role in computationally representing and interpreting narratives.
