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Found 33 papers, 15 papers with code
CULTURE-GEN: Revealing Global Cultural Perception in Language Models through Natural Language Prompting
As the utilization of large language models (LLMs) has proliferated worldwide, it is crucial for them to have adequate knowledge and fair representation for diverse global cultures.
CulturalTeaming: AI-Assisted Interactive Red-Teaming for Challenging LLMs' (Lack of) Multicultural Knowledge
Our study reveals that CulturalTeaming's various modes of AI assistance support annotators in creating cultural questions, that modern LLMs fail at, in a gamified manner.
Particip-AI: A Democratic Surveying Framework for Anticipating Future AI Use Cases, Harms and Benefits
As a first step towards democratic governance and risk assessment of AI, we introduce Particip-AI, a framework to gather current and future AI use cases and their harms and benefits from non-expert public.
Information-Theoretic Distillation for Reference-less Summarization
The current winning recipe for automatic summarization is using proprietary large-scale language models (LLMs) such as ChatGPT as is, or imitation learning from them as teacher models.
JAMDEC: Unsupervised Authorship Obfuscation using Constrained Decoding over Small Language Models
The permanence of online content combined with the enhanced authorship identification techniques calls for stronger computational methods to protect the identity and privacy of online authorship when needed, e. g., blind reviews for scientific papers, anonymous online reviews, or anonymous interactions in the mental health forums.
A Roadmap to Pluralistic Alignment
We identify and formalize three possible ways to define and operationalize pluralism in AI systems: 1) Overton pluralistic models that present a spectrum of reasonable responses; 2) Steerably pluralistic models that can steer to reflect certain perspectives; and 3) Distributionally pluralistic models that are well-calibrated to a given population in distribution.
NovaCOMET: Open Commonsense Foundation Models with Symbolic Knowledge Distillation
We present NovaCOMET, an open commonsense knowledge model, that combines the best aspects of knowledge and general task models.
The Generative AI Paradox: "What It Can Create, It May Not Understand"
Specifically, we propose and test the Generative AI Paradox hypothesis: generative models, having been trained directly to reproduce expert-like outputs, acquire generative capabilities that are not contingent upon -- and can therefore exceed -- their ability to understand those same types of outputs.
What Makes it Ok to Set a Fire? Iterative Self-distillation of Contexts and Rationales for Disambiguating Defeasible Social and Moral Situations
From this model we distill a high-quality dataset, \delta-Rules-of-Thumb, of 1. 2M entries of contextualizations and rationales for 115K defeasible moral actions rated highly by human annotators 85. 9% to 99. 8% of the time.
Reading Books is Great, But Not if You Are Driving! Visually Grounded Reasoning about Defeasible Commonsense Norms
NORMLENS consists of 10K human judgments accompanied by free-form explanations covering 2K multimodal situations, and serves as a probe to address two questions: (1) to what extent can models align with average human judgment?
Phenomenal Yet Puzzling: Testing Inductive Reasoning Capabilities of Language Models with Hypothesis Refinement
The ability to derive underlying principles from a handful of observations and then generalize to novel situations -- known as inductive reasoning -- is central to human intelligence.
Value Kaleidoscope: Engaging AI with Pluralistic Human Values, Rights, and Duties
To improve AI systems to better reflect value pluralism, the first-order challenge is to explore the extent to which AI systems can model pluralistic human values, rights, and duties as well as their interaction.
Faith and Fate: Limits of Transformers on Compositionality
We formulate compositional tasks as computation graphs to systematically quantify the level of complexity, and break down reasoning steps into intermediate sub-procedures.
Impossible Distillation: from Low-Quality Model to High-Quality Dataset & Model for Summarization and Paraphrasing
We present Impossible Distillation, a novel framework for paraphrasing and sentence summarization, that distills a high-quality dataset and model from a low-quality teacher that itself cannot perform these tasks.
Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuning
While extreme-scale language models have demonstrated exceptional performance on a variety of language tasks, the degree of control over these language models through pure prompting can often be limited.
BiasX: "Thinking Slow" in Toxic Content Moderation with Explanations of Implied Social Biases
Toxicity annotators and content moderators often default to mental shortcuts when making decisions.
Asymptotic normality and optimality in nonsmooth stochastic approximation
In their seminal work, Polyak and Juditsky showed that stochastic approximation algorithms for solving smooth equations enjoy a central limit theorem.
ClarifyDelphi: Reinforced Clarification Questions with Defeasibility Rewards for Social and Moral Situations
Context is everything, even in commonsense moral reasoning.
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.
A Validation Approach to Over-parameterized Matrix and Image Recovery
In this paper, we study the problem of recovering a low-rank matrix from a number of noisy random linear measurements.
Quark: Controllable Text Generation with Reinforced Unlearning
Large-scale language models often learn behaviors that are misaligned with user expectations.
ProsocialDialog: A Prosocial Backbone for Conversational Agents
With this dataset, we introduce a dialogue safety detection module, Canary, capable of generating RoTs given conversational context, and a socially-informed dialogue agent, Prost.
Ranked #1 on
Dialogue Safety Prediction
on ProsocialDialog
Aligning to Social Norms and Values in Interactive Narratives
We focus on creating agents that act in alignment with socially beneficial norms and values in interactive narratives or text-based games -- environments wherein an agent perceives and interacts with a world through natural language.
Algorithmic Regularization in Model-free Overparametrized Asymmetric Matrix Factorization
We study the asymmetric matrix factorization problem under a natural nonconvex formulation with arbitrary overparametrization.
NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics
To enable constrained generation, we build on NeuroLogic decoding (Lu et al., 2021), combining its flexibility in incorporating logical constraints with A*esque estimates of future constraint satisfaction.
Ranked #1 on
Text Generation
on ROCStories
Symbolic Knowledge Distillation: from General Language Models to Commonsense Models
We apply this to the ATOMIC resource, and share our new symbolic knowledge graph and commonsense models.
Can Machines Learn Morality? The Delphi Experiment
As AI systems become increasingly powerful and pervasive, there are growing concerns about machines' morality or a lack thereof.
Rank Overspecified Robust Matrix Recovery: Subgradient Method and Exact Recovery
We study the robust recovery of a low-rank matrix from sparsely and grossly corrupted Gaussian measurements, with no prior knowledge on the intrinsic rank.
Active manifolds, stratifications, and convergence to local minima in nonsmooth optimization
We show that the subgradient method converges only to local minimizers when applied to generic Lipschitz continuous and subdifferentially regular functions that are definable in an o-minimal structure.
``I'm Not Mad'': Commonsense Implications of Negation and Contradiction
In this paper, we present the first comprehensive study focusing on commonsense implications of negated statements and contradictions.
"I'm Not Mad": Commonsense Implications of Negation and Contradiction
In this paper, we present the first comprehensive study focusing on commonsense implications of negated statements and contradictions.
AUL is a better optimization metric in PU learning
Recent works try to recover the unbiased result by estimating the proportion of positive samples with mixture proportion estimation (MPE) algorithms, but the model performance is still limited and heavy computational cost is introduced (particularly for big datasets).
Improving Positive Unlabeled Learning: Practical AUL Estimation and New Training Method for Extremely Imbalanced Data Sets
Secondly, we propose ProbTagging, a new training method for extremely imbalanced data sets, where the number of unlabeled samples is hundreds or thousands of times that of positive samples.
