Optimal Reward Labeling: Bridging Offline Preference and Reward-Based Reinforcement Learning

Offline reinforcement learning has become one of the most practical RL settings. A recent success story has been RLHF, offline preference-based RL (PBRL) with preference from humans. However, most existing works on offline RL focus on the standard setting with scalar reward feedback. It remains unknown how to universally transfer the existing rich understanding of offline RL from the reward-based to the preference-based setting. In this work, we propose a general framework to bridge this gap. Our key insight is transforming preference feedback to scalar rewards via optimal reward labeling (ORL), and then any reward-based offline RL algorithms can be applied to the dataset with the reward labels. We theoretically show the connection between several recent PBRL techniques and our framework combined with specific offline RL algorithms in terms of how they utilize the preference signals. By combining reward labeling with different algorithms, our framework can lead to new and potentially more efficient offline PBRL algorithms. We empirically test our framework on preference datasets based on the standard D4RL benchmark. When combined with a variety of efficient reward-based offline RL algorithms, the learning result achieved under our framework is comparable to training the same algorithm on the dataset with actual rewards in many cases and better than the recent PBRL baselines in most cases.

Enhancing Sign Language Detection through Mediapipe and Convolutional Neural Networks (CNN)

This research combines MediaPipe and CNNs for the efficient and accurate interpretation of ASL dataset for the real-time detection of sign language. The system presented here captures and processes hands' gestures in real time. the intended purpose was to create a very easy, accurate, and fast way of entering commands without the necessity of touching something.MediaPipe supports one of the powerful frameworks in real-time hand tracking capabilities for the ability to capture and preprocess hand movements, which increases the accuracy of the gesture recognition system. Actually, the integration of CNN with the MediaPipe results in higher efficiency in using the model of real-time processing.The accuracy achieved by the model on ASL datasets is 99.12\%.The model was tested using American Sign Language (ASL) datasets. The results were then compared to those of existing methods to evaluate how well it performed, using established evaluation techniques. The system will have applications in the communication, education, and accessibility domains. Making systems such as described in this paper even better will assist people with hearing impairment and make things accessible to them. We tested the recognition and translation performance on an ASL dataset and achieved better accuracy over previous models.It is meant to the research is to identify the characters that American signs recognize using hand images taken from a web camera by based on mediapipe and CNNs

Quantitative Certification of Bias in Large Language Models

Large Language Models (LLMs) can produce responses that exhibit social biases and support stereotypes. However, conventional benchmarking is insufficient to thoroughly evaluate LLM bias, as it can not scale to large sets of prompts and provides no guarantees. Therefore, we propose a novel certification framework QuaCer-B (Quantitative Certification of Bias) that provides formal guarantees on obtaining unbiased responses from target LLMs under large sets of prompts. A certificate consists of high-confidence bounds on the probability of obtaining biased responses from the LLM for any set of prompts containing sensitive attributes, sampled from a distribution. We illustrate the bias certification in LLMs for prompts with various prefixes drawn from given distributions. We consider distributions of random token sequences, mixtures of manual jailbreaks, and jailbreaks in the LLM's embedding space to certify its bias. We certify popular LLMs with QuaCer-B and present novel insights into their biases.

Relational DNN Verification With Cross Executional Bound Refinement

We focus on verifying relational properties defined over deep neural networks (DNNs) such as robustness against universal adversarial perturbations (UAP), certified worst-case hamming distance for binary string classifications, etc. Precise verification of these properties requires reasoning about multiple executions of the same DNN. However, most of the existing works in DNN verification only handle properties defined over single executions and as a result, are imprecise for relational properties. Though few recent works for relational DNN verification, capture linear dependencies between the inputs of multiple executions, they do not leverage dependencies between the outputs of hidden layers producing imprecise results. We develop a scalable relational verifier RACoon that utilizes cross-execution dependencies at all layers of the DNN gaining substantial precision over SOTA baselines on a wide range of datasets, networks, and relational properties.

Cross-Input Certified Training for Universal Perturbations

Existing work in trustworthy machine learning primarily focuses on single-input adversarial perturbations. In many real-world attack scenarios, input-agnostic adversarial attacks, e.g. universal adversarial perturbations (UAPs), are much more feasible. Current certified training methods train models robust to single-input perturbations but achieve suboptimal clean and UAP accuracy, thereby limiting their applicability in practical applications. We propose a novel method, CITRUS, for certified training of networks robust against UAP attackers. We show in an extensive evaluation across different datasets, architectures, and perturbation magnitudes that our method outperforms traditional certified training methods on standard accuracy (up to 10.3\%) and achieves SOTA performance on the more practical certified UAP accuracy metric.

Is Watermarking LLM-Generated Code Robust?

We present the first study of the robustness of existing watermarking techniques on Python code generated by large language models. Although existing works showed that watermarking can be robust for natural language, we show that it is easy to remove these watermarks on code by semantic-preserving transformations.

Improving LLM Code Generation with Grammar Augmentation

We present SynCode a novel framework for efficient and general syntactical decoding of code with large language models (LLMs). SynCode leverages the grammar of a programming language, utilizing an offline-constructed efficient lookup table called DFA mask store based on language grammar terminals. We demonstrate SynCode's soundness and completeness given the context-free grammar (CFG) of the programming language, presenting its ability to retain syntactically valid tokens while rejecting invalid ones. The framework seamlessly integrates with any language defined by CFG, as evidenced by experiments on CFGs for Python and Go. The results underscore the significant reduction of 96.07% of syntax errors achieved when SynCode is combined with state-of-the-art LLMs, showcasing its substantial impact on enhancing syntactical precision in code generation. Our code is available at https://github.com/uiuc-focal-lab/syncode.

QuaCer-C: Quantitative Certification of Knowledge Comprehension in LLMs

Large Language Models (LLMs) have demonstrated impressive performance on several benchmarks. However, traditional studies do not provide formal guarantees on the performance of LLMs. In this work, we propose a novel certification framework for LLM, QuaCer-C, wherein we formally certify the knowledge-comprehension capabilities of popular LLMs. Our certificates are quantitative - they consist of high-confidence, tight bounds on the probability that the target LLM gives the correct answer on any relevant knowledge comprehension prompt. Our certificates for the Llama, Vicuna, and Mistral LLMs indicate that the knowledge comprehension capability improves with an increase in the number of parameters and that the Mistral model is less performant than the rest in this evaluation.

Reward Poisoning Attack Against Offline Reinforcement Learning

We study the problem of reward poisoning attacks against general offline reinforcement learning with deep neural networks for function approximation. We consider a black-box threat model where the attacker is completely oblivious to the learning algorithm and its budget is limited by constraining both the amount of corruption at each data point, and the total perturbation. We propose an attack strategy called `policy contrast attack'. The high-level idea is to make some low-performing policies appear as high-performing while making high-performing policies appear as low-performing. To the best of our knowledge, we propose the first black-box reward poisoning attack in the general offline RL setting. We provide theoretical insights on the attack design and empirically show that our attack is efficient against current state-of-the-art offline RL algorithms in different kinds of learning datasets.

RAMP: Boosting Adversarial Robustness Against Multiple $l_p$ Perturbations

There is considerable work on improving robustness against adversarial attacks bounded by a single $l_p$ norm using adversarial training (AT). However, the multiple-norm robustness (union accuracy) of AT models is still low. We observe that simultaneously obtaining good union and clean accuracy is hard since there are tradeoffs between robustness against multiple $l_p$ perturbations, and accuracy/robustness/efficiency. By analyzing the tradeoffs from the lens of distribution shifts, we identify the key tradeoff pair among $l_p$ attacks to boost efficiency and design a logit pairing loss to improve the union accuracy. Next, we connect natural training with AT via gradient projection, to find and incorporate useful information from natural training into AT, which moderates the accuracy/robustness tradeoff. Combining our contributions, we propose a framework called \textbf{RAMP}, to boost the robustness against multiple $l_p$ perturbations. We show \textbf{RAMP} can be easily adapted for both robust fine-tuning and full AT. For robust fine-tuning, \textbf{RAMP} obtains a union accuracy up to $53.5\%$ on CIFAR-10, and $29.7\%$ on ImageNet. For training from scratch, \textbf{RAMP} achieves SOTA union accuracy of $44.6\%$ and relatively good clean accuracy of $81.2\%$ on ResNet-18 against AutoAttack on CIFAR-10.