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Found 32 papers, 8 papers with code
Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference
To address this issue, we introduce Chatbot Arena, an open platform for evaluating LLMs based on human preferences.
Generative AI Security: Challenges and Countermeasures
Generative AI's expanding footprint across numerous industries has led to both excitement and increased scrutiny.
Efficient Prompt Caching via Embedding Similarity
In this paper, we aim to improve the inference efficiency of LLMs by prompt caching, i. e., if the current prompt can be answered by the same response of a previous prompt, one can directly utilize that previous response without calling the LLM.
Iterative Data Smoothing: Mitigating Reward Overfitting and Overoptimization in RLHF
Reinforcement Learning from Human Feedback (RLHF) is a pivotal technique that aligns language models closely with human-centric values.
Fairness in Serving Large Language Models
High-demand LLM inference services (e. g., ChatGPT and BARD) support a wide range of requests from short chat conversations to long document reading.
Towards Optimal Statistical Watermarking
Key to our formulation is a coupling of the output tokens and the rejection region, realized by pseudo-random generators in practice, that allows non-trivial trade-offs between the Type I error and Type II error.
The Effective Horizon Explains Deep RL Performance in Stochastic Environments
Our goal is to explain why deep RL algorithms often perform well in practice, despite using random exploration and much more expressive function classes like neural networks.
S-LoRA: Serving Thousands of Concurrent LoRA Adapters
To capitalize on these opportunities, we present S-LoRA, a system designed for the scalable serving of many LoRA adapters.
Towards the Fundamental Limits of Knowledge Transfer over Finite Domains
The second level has the teacher probabilities of sampled labels available in addition, which turns out to boost the convergence rate lower bound to ${{|{\mathcal S}||{\mathcal A}|}/{n}}$.
QFT: Quantized Full-parameter Tuning of LLMs with Affordable Resources
Large Language Models (LLMs) have showcased remarkable impacts across a wide spectrum of natural language processing tasks.
Pairwise Proximal Policy Optimization: Harnessing Relative Feedback for LLM Alignment
In summary, this work introduces a simpler yet effective approach for aligning LLMs to human preferences through relative feedback.
Guided Online Distillation: Promoting Safe Reinforcement Learning by Offline Demonstration
GOLD distills an offline DT policy into a lightweight policy network through guided online safe RL training, which outperforms both the offline DT policy and online safe RL algorithms.
Noisy Computing of the $\mathsf{OR}$ and $\mathsf{MAX}$ Functions
We consider the problem of computing a function of $n$ variables using noisy queries, where each query is incorrect with some fixed and known probability $p \in (0, 1/2)$.
On the Optimal Bounds for Noisy Computing
However, the upper and lower bounds do not match in terms of the dependence on $\delta$ and $p$.
Fine-Tuning Language Models with Advantage-Induced Policy Alignment
Reinforcement learning from human feedback (RLHF) has emerged as a reliable approach to aligning large language models (LLMs) to human preferences.
On Optimal Caching and Model Multiplexing for Large Model Inference
Theoretically, we provide an optimal algorithm for jointly optimizing both approaches to reduce the inference cost in both offline and online tabular settings.
Doubly Robust Self-Training
Self-training is an important technique for solving semi-supervised learning problems.
Online Learning in a Creator Economy
In this paper, we initiate the study of online learning in the creator economy by modeling the creator economy as a three-party game between the users, platform, and content creators, with the platform interacting with the content creator under a principal-agent model through contracts to encourage better content.
Online Learning in Stackelberg Games with an Omniscient Follower
We analyze the sample complexity of regret minimization in this repeated Stackelberg game.
Principled Reinforcement Learning with Human Feedback from Pairwise or $K$-wise Comparisons
Our analysis shows that when the true reward function is linear, the widely used maximum likelihood estimator (MLE) converges under both the Bradley-Terry-Luce (BTL) model and the Plackett-Luce (PL) model.
The Sample Complexity of Online Contract Design
This result shows that exponential-in-$m$ samples are sufficient and necessary to learn a near-optimal contract, resolving an open problem on the hardness of online contract design.
Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guarantees
In contrast to prior work, our proposed protocols improve the dimension dependence and achieve a tight statistical rate in terms of all the parameters for strongly convex losses.
Jump-Start Reinforcement Learning
In addition, we provide an upper bound on the sample complexity of JSRL and show that with the help of a guide-policy, one can improve the sample complexity for non-optimism exploration methods from exponential in horizon to polynomial.
Robust Estimation for Nonparametric Families via Generative Adversarial Networks
Prior work focus on the problem of robust mean and covariance estimation when the true distribution lies in the family of Gaussian distributions or elliptical distributions, and analyze depth or scoring rule based GAN losses for the problem.
Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism
Based on the composition of the offline dataset, two main categories of methods are used: imitation learning which is suitable for expert datasets and vanilla offline RL which often requires uniform coverage datasets.
Minimax Off-Policy Evaluation for Multi-Armed Bandits
Second, when the behavior policy is unknown, we analyze performance in terms of the competitive ratio, thereby revealing a fundamental gap between the settings of known and unknown behavior policies.
Linear Representation Meta-Reinforcement Learning for Instant Adaptation
This paper introduces Fast Linearized Adaptive Policy (FLAP), a new meta-reinforcement learning (meta-RL) method that is able to extrapolate well to out-of-distribution tasks without the need to reuse data from training, and adapt almost instantaneously with the need of only a few samples during testing.
Robust estimation via generalized quasi-gradients
We study the loss landscape of these robust estimation problems, and identify the existence of "generalized quasi-gradients".
When does the Tukey median work?
We show that under TV corruptions, the breakdown point reduces to 1/4 for the same set of distributions.
Generalized Resilience and Robust Statistics
This generalizes a property called resilience previously employed in the special case of mean estimation with outliers.
Deconstructing Generative Adversarial Networks
Generalization: given a population target of GANs, we design a systematic principle, projection under admissible distance, to design GANs to meet the population requirement using finite samples.
Joint Transceiver Optimization for Wireless Communication PHY with Convolutional Neural Network
The simulation results show that the performance of neural network based system is superior to traditional modulation and equalization methods in terms of time complexity and bit error rate (BER) under fading channels.
