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Found 11 papers, 3 papers with code
Chain of Agents: Large Language Models Collaborating on Long-Context Tasks
Addressing the challenge of effectively processing long contexts has become a critical issue for Large Language Models (LLMs).
Learning to Clarify: Multi-turn Conversations with Action-Based Contrastive Self-Training
Large language models (LLMs) aligned through reinforcement learning from human feedback (RLHF) have quickly become one of the dominant paradigms for building intelligent conversational assistant agents.
Mitigating Object Hallucination via Data Augmented Contrastive Tuning
For a given factual token, we create a hallucinated token through generative data augmentation by selectively altering the ground-truth information.
Effective Large Language Model Adaptation for Improved Grounding and Citation Generation
Our framework tunes LLMs to selfground the claims in their responses and provide accurate citations to retrieved documents.
SQLPrompt: In-Context Text-to-SQL with Minimal Labeled Data
Text-to-SQL aims to automate the process of generating SQL queries on a database from natural language text.
COSTAR: Improved Temporal Counterfactual Estimation with Self-Supervised Learning
Estimation of temporal counterfactual outcomes from observed history is crucial for decision-making in many domains such as healthcare and e-commerce, particularly when randomized controlled trials (RCTs) suffer from high cost or impracticality.
SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL (extended)
Text-to-SQL, the process of translating natural language into Structured Query Language (SQL), represents a transformative application of large language models (LLMs), potentially revolutionizing how humans interact with data.
Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts
Temporal distributional shifts, with underlying dynamics changing over time, frequently occur in real-world time series and pose a fundamental challenge for deep neural networks (DNNs).
Self-Supervised Learning with an Information Maximization Criterion
In this article, we argue that a straightforward application of information maximization among alternative latent representations of the same input naturally solves the collapse problem and achieves competitive empirical results.
Ranked #1 on
Self-Supervised Learning
on ImageNet-100
Invariant Structure Learning for Better Generalization and Causal Explainability
ISL splits the data into different environments, and learns a structure that is invariant to the target across different environments by imposing a consistency constraint.
Interpretable Mixture of Experts
In addition to constituting a standalone inherently-interpretable architecture, IME has the premise of being integrated with existing DNNs to offer interpretability to a subset of samples while maintaining the accuracy of the DNNs.
