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Found 22 papers, 8 papers with code
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
It is commonly perceived that the strongest language models (LMs) rely on a combination of massive scale, instruction data, and human feedback to perform specialized tasks -- e. g. summarization and paraphrasing, without supervision.
Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuning
In particular, tailoring GPT-2 with IPA can outperform GPT-3, while tailoring GPT- 3 with IPA brings a major performance boost over GPT-3 (and sometimes even over GPT-4).
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
We present SODA: the first publicly available, million-scale high-quality social dialogue dataset.
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.
