GenAI4ELab/papercli-papers huggingface dataset:
Refusal in Language Models Is Mediated by a Single Direction
Authors: Andy Arditi; Oscar Balcells Obeso; Aaquib Syed; Daniel Paleka; Nina Rimsky; Wes Gurnee; Neel Nanda
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Conversational large language models are fine-tuned for both instruction-following and safety, resulting in models that obey benign requests but refuse harmful ones. While this refusal behavior is widespread across chat models, its underlying mechanisms remain poorly understood. In this work, we show that refusal is mediated by a one-dimensional subspace, across 13 popular open-source chat models up to 72B parameters in size. Specifically, for each model, we find a single direction such that erasing this direction from the model's residual stream activations prevents it from refusing harmful instructions, while adding this direction elicits refusal on even harmless instructions. Leveraging this insight, we propose a novel white-box jailbreak method that surgically disables a model's ability to refuse, with minimal effect on other capabilities. This interpretable rank-one weight edit results in an effective jailbreak technique that is simpler and more efficient than fine-tuning. Finally, we mechanistically analyze how adversarial suffixes suppress propagation of the refusal-mediating direction. Our findings underscore the brittleness of current safety fine-tuning methods. More broadly, our work showcases how an understanding of model internals can be leveraged to develop practical methods for controlling model behavior.
Trace is the Next AutoDiff: Generative Optimization with Rich Feedback, Execution Traces, and LLMs
Authors: Ching-An Cheng; Allen Nie; Adith Swaminathan
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: We study a class of optimization problems motivated by automating the design and update of AI systems like coding assistants, robots, and copilots. AutoDiff frameworks, like PyTorch, enable efficient end-to-end optimization of differentiable systems. However, general computational workflows can be non-differentiable and involve rich feedback (e.g. console output or user’s responses), heterogeneous parameters (e.g. prompts, codes), and intricate objectives (beyond maximizing a score). We investigate end-to-end generative optimization – using generative models such as LLMs within the optimizer for automatic updating of general computational workflows. We discover that workflow execution traces are akin to back-propagated gradients in AutoDiff and can provide key information to interpret feedback for efficient optimization. Formally, we frame a new mathematical setup, Optimization with Trace Oracle (OPTO). In OPTO, an optimizer receives an execution trace along with feedback on the computed output and updates parameters iteratively. We provide a Python library, Trace, that efficiently converts a workflow optimization problem into an OPTO instance using PyTorch-like syntax. Using Trace, we develop a general LLM-based generative optimizer called OptoPrime. In empirical studies, we find that OptoPrime is capable of first-order numerical optimization, prompt optimization, hyper-parameter tuning, robot controller design, code debugging, etc., and is often competitive with specialized optimizers for each domain. We envision Trace as an open research platform for devising novel generative optimizers and developing the next generation of interactive learning agents. Website: https://microsoft.github.io/Trace/.
SIRIUS : Contexual Sparisty with Correction for Efficient LLMs
Authors: Yang Zhou; Zhuoming Chen; Zhaozhuo Xu; Xi Victoria Lin; Beidi Chen
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: With the blossom of large language models (LLM), inference efficiency becomes increasingly important. Various approximate methods are proposed to reduce the cost at inference time. Contextual Sparsity (CS) is appealing for its training-free nature and its ability to reach a higher compression ratio seemingly without significant performance degradation. However, after a comprehensive evaluation of contextual sparsity methods on various complex generation tasks, we find that although CS succeeds in prompt-understanding tasks, it significantly degrades the model performance for reasoning, deduction, and knowledge-based tasks. Despite the gap in end-to-end accuracy, we observed that sparse models and original models often share the general problem-solving logic and require only a few token corrections to recover the original model performance. This paper introduces SIRIUS, an efficient correction mechanism, which significantly boosts CS models on reasoning tasks while maintaining its efficiency gain. SIRIUS is evaluated on 6 models with 8 difficult generation tasks in reasoning, deduction, and coding and shows consistent effectiveness and efficiency. Also, we carefully develop a system implementation for SIRIUS and show that SIRIUS delivers theoretical latency reduction with roughly a 20% reduction in latency for 8B model on-chip and a 35% reduction in latency for 70B model offloading. We open-source our implementation of Sirius at https://github.com/Infini-AI-Lab/Sirius.git.
Attack-Aware Noise Calibration for Differential Privacy
Authors: Bogdan Kulynych; Juan Felipe Gomez; Georgios Kaissis; Flavio Calmon; Carmela Troncoso
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Differential privacy (DP) is a widely used approach for mitigating privacy risks when training machine learning models on sensitive data. DP mechanisms add noise during training to limit the risk of information leakage. The scale of the added noise is critical, as it determines the trade-off between privacy and utility. The standard practice is to select the noise scale to satisfy a given privacy budget ε. This privacy budget is in turn interpreted in terms of operational attack risks, such as accuracy, sensitivity, and specificity of inference attacks aimed to recover
information about the training data records. We show that first calibrating the noise scale to a privacy budget ε, and then translating ε to attack risk leads to overly conservative risk assessments and unnecessarily low utility. Instead, we propose methods to directly calibrate the noise scale to a desired attack risk level, bypassing the step of choosing ε. For a given notion of attack risk, our approach significantly
decreases noise scale, leading to increased utility at the same level of privacy. We empirically demonstrate that calibrating noise to attack sensitivity/specificity, rather than ε, when training privacy-preserving ML models substantially improves model accuracy for the same risk level. Our work provides a principled and practical way to improve the utility of privacy-preserving ML without compromising on privacy.
Fairness in Social Influence Maximization via Optimal Transport
Authors: Shubham Chowdhary; Giulia De Pasquale; Nicolas Lanzetti; Ana-Andreea Stoica; Florian Dorfler
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: We study fairness in social influence maximization, whereby one seeks to select
seeds that spread a given information throughout a network, ensuring balanced
outreach among different communities (e.g. demographic groups). In the literature,
fairness is often quantified in terms of the expected outreach within individual
communities. In this paper, we demonstrate that such fairness metrics can be
misleading since they overlook the stochastic nature of information diffusion
processes. When information diffusion occurs in a probabilistic manner, multiple
outreach scenarios can occur. As such, outcomes such as “In 50% of the cases, no
one in group 1 gets the information, while everyone in group 2 does, and in the
other 50%, it is the opposite”, which always results in largely unfair outcomes,
are classified as fair by a variety of fairness metrics in the literature. We tackle
this problem by designing a new fairness metric, mutual fairness, that captures
variability in outreach through optimal transport theory. We propose a new seed-
selection algorithm that optimizes both outreach and mutual fairness, and we show
its efficacy on several real datasets. We find that our algorithm increases fairness
with only a minor decrease (and at times, even an increase) in efficiency.
A Critical Evaluation of AI Feedback for Aligning Large Language Models
Authors: Archit Sharma; Sedrick Keh; Eric Mitchell; Chelsea Finn; Kushal Arora; Thomas Kollar
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Learning from AI feedback (LAIF) is a popular paradigm for improving the instruction-following abilities of powerful pre-trained language models. LAIF first performs supervised fine-tuning (SFT) using demonstrations from a teacher model and then further fine-tunes the model with reinforcement learning (RL) or direct preference optimization (DPO), using feedback from a critic model. While recent popular open-source models have demonstrated substantial improvements in performance from the RL step, in this paper we question whether the complexity of this RL step is truly warranted for AI feedback. We show that the improvements of the RL step are virtually entirely due to the widespread practice of using a weaker teacher model (e.g. GPT-3.5) for SFT data collection than the critic (e.g., GPT-4) used for AI feedback generation. Specifically, we show that simple supervised fine-tuning with GPT-4 as the teacher outperforms existing LAIF pipelines. More generally, we find that the gains from LAIF vary substantially across base model families, test-time evaluation protocols, and critic models. Finally, we provide a mechanistic explanation for when SFT may outperform the full two-step LAIF pipeline as well as suggestions for making LAIF maximally useful in practice.
Paths to Equilibrium in Games
Authors: Bora Yongacoglu; Gurdal Arslan; Lacra Pavel; Serdar Yuksel
Venue: NeurIPS 2024 (NeurIPS 2024 spotlight)

Abstract: In multi-agent reinforcement learning (MARL) and game theory, agents repeatedly interact and revise their strategies as new data arrives, producing a sequence of strategy profiles. This paper studies sequences of strategies satisfying a pairwise constraint inspired by policy updating in reinforcement learning, where an agent who is best responding in one period does not switch its strategy in the next period. This constraint merely requires that optimizing agents do not switch strategies, but does not constrain the non-optimizing agents in any way, and thus allows for exploration. Sequences with this property are called satisficing paths, and arise naturally in many MARL algorithms.  A fundamental question about strategic dynamics is such: for a given game and initial strategy profile, is it always possible to construct a satisficing path that terminates at an equilibrium? The resolution of this question has implications about the capabilities or limitations of a class of MARL algorithms. We answer this question in the affirmative for normal-form games. Our analysis reveals a counterintuitive insight that suboptimal, and perhaps even reward deteriorating, strategic updates are key to driving play to equilibrium along a satisficing path.
Tangent Space Causal Inference: Leveraging Vector Fields for Causal Discovery in Dynamical Systems
Authors: Kurt Butler; Daniel Waxman; Petar Djuric
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Causal discovery with time series data remains a challenging yet increasingly important task across many scientific domains. Convergent cross mapping (CCM) and related methods have been proposed to study time series that are generated by dynamical systems, where traditional approaches like Granger causality are unreliable. However, CCM often yields inaccurate results depending upon the quality of the data. We propose the Tangent Space Causal Inference (TSCI) method for detecting causalities in dynamical systems. TSCI works by considering vector fields as explicit representations of the systems' dynamics and checks for the degree of synchronization between the learned vector fields. The TSCI approach is model-agnostic and can be used as a drop-in replacement for CCM and its generalizations. We first present a basic version of the TSCI algorithm, which is shown to be more effective than the basic CCM algorithm with very little additional computation. We additionally present augmented versions of TSCI that leverage the expressive power of latent variable models and deep learning. We validate our theory on standard systems, and we demonstrate improved causal inference performance across a number of benchmark tasks.
Towards Effective Planning Strategies for Dynamic Opinion Networks
Authors: Bharath Chandra Muppasani; Protik Nag; Vignesh Narayanan; Biplav Srivastava; Michael Huhns
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: In this study, we investigate the under-explored intervention planning aimed at disseminating accurate information within dynamic opinion networks by leveraging learning strategies. Intervention planning involves identifying key nodes (search) and exerting control (e.g., disseminating accurate/official information through the nodes) to mitigate the influence of misinformation. However, as the network size increases, the problem becomes computationally intractable. To address this, we first introduce a ranking algorithm to identify key nodes for disseminating accurate information, which facilitates the training of neural network (NN) classifiers that provide generalized solutions for the search and planning problems. Second, we mitigate the complexity of label generation—which becomes challenging as the network grows—by developing a reinforcement learning (RL)-based centralized dynamic planning framework. We analyze these NN-based planners for opinion networks governed by two dynamic propagation models. Each model incorporates both binary and continuous opinion and trust representations. Our experimental results demonstrate that the ranking algorithm-based classifiers provide plans that enhance infection rate control, especially with increased action budgets for small networks. Further, we observe that the reward strategies focusing on key metrics, such as the number of susceptible nodes and infection rates, outperform those prioritizing faster blocking strategies. Additionally, our findings reveal that graph convolutional network (GCN)-based planners facilitate scalable centralized plans that achieve lower infection rates (higher control) across various network configurations (e.g., Watts-Strogatz topology, varying action budgets, varying initial infected nodes, and varying degree of infected nodes).
Universality of AdaGrad Stepsizes for Stochastic Optimization: Inexact Oracle, Acceleration and Variance Reduction
Authors: Anton Rodomanov; Xiaowen Jiang; Sebastian U Stich
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: We present adaptive gradient methods (both basic and accelerated) for solving
convex composite optimization problems in which the main part is approximately
smooth (a.k.a. $(\delta, L)$-smooth) and can be accessed only via a
(potentially biased) stochastic gradient oracle.
This setting covers many interesting examples including Hölder smooth problems
and various inexact computations of the stochastic gradient.
Our methods use AdaGrad stepsizes and are adaptive in the sense that they do
not require knowing any problem-dependent constants except an estimate of the
diameter of the feasible set but nevertheless achieve the best possible
convergence rates as if they knew the corresponding constants.
We demonstrate that AdaGrad stepsizes work in a variety of situations
by proving, in a unified manner, three types of new results.
First, we establish efficiency guarantees for our methods in the classical
setting where the oracle's variance is uniformly bounded.
We then show that, under more refined assumptions on the variance,
the same methods without any modifications enjoy implicit variance
reduction properties allowing us to express their complexity estimates in
terms of the variance only at the minimizer.
Finally, we show how to incorporate explicit SVRG-type variance reduction into
our methods and obtain even faster algorithms.
In all three cases, we present both basic and accelerated algorithms
achieving state-of-the-art complexity bounds.
As a direct corollary of our results, we obtain universal stochastic gradient
methods for Hölder smooth problems which can be used in all situations.
Proportional Fairness in Non-Centroid Clustering
Authors: Ioannis Caragiannis; Evi Micha; Nisarg Shah
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: We revisit the recently developed framework of proportionally fair clustering, where the goal is to provide group fairness guarantees that become stronger for groups of data points that are large and cohesive. Prior work applies this framework to centroid-based clustering, where points are partitioned into clusters, and the cost to each data point is measured by its distance to a centroid assigned to its cluster. However, real-life applications often do not require such centroids. We extend the theory of proportionally fair clustering to non-centroid clustering by considering a variety of cost functions, both metric and non-metric, for a data point to be placed in a cluster with other data points. Our results indicate that Greedy Capture, a clustering algorithm developed for centroid clustering, continues to provide strong proportional fairness guarantees for non-centroid clustering, although the guarantees are significantly different and establishing them requires novel proof ideas. We also design algorithms for auditing proportional fairness of a given clustering solution. We conduct experiments on real data which suggest that traditional clustering algorithms are highly unfair, while our algorithms achieve strong fairness guarantees with a moderate loss in common clustering objectives.
Unified Insights: Harnessing Multi-modal Data for Phenotype Imputation via View Decoupling
Authors: Qiannan Zhang; Weishen Pan; Zilong Bai; Chang Su; Fei Wang
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Phenotype imputation plays a crucial role in improving comprehensive and accurate medical evaluation, which in turn can optimize patient treatment and bolster the reliability of clinical research. Despite the adoption of various techniques, multi-modal biological data, which can provide crucial insights into a patient's overall health, is often overlooked. With multi-modal biological data, patient characterization can be enriched from two distinct views: the biological view and the phenotype view. However, the heterogeneity and imprecise nature of the multimodal data still pose challenges in developing an effective method to model from two views. In this paper, we propose a novel framework to incorporate multi-modal biological data via view decoupling. Specifically, we segregate the modeling of biological data from phenotype data in a graph-based learning framework. From the biological view, the latent factors in biological data are discovered to model patient correlation. From the phenotype view, phenotype co-occurrence can be modeled to reveal patterns across patients. Then patients are encoded from these two distinct views. To mitigate the influence of noise and irrelevant information in biological data, we devise a cross-view contrastive knowledge distillation aimed at distilling insights from the biological view to enhance phenotype imputation. We show that phenotype imputation with the proposed model significantly outperforms the state-of-the-art models on the real-world biomedical database.
Incentivizing Quality Text Generation via Statistical Contracts
Authors: Eden Saig; Ohad Einav; Inbal Talgam-Cohen
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: While the success of large language models (LLMs) increases demand for machine-generated text, current pay-per-token pricing schemes create a misalignment of incentives known in economics as moral hazard: Text-generating agents have strong incentive to cut costs by preferring a cheaper model over the cutting-edge one, and this can be done “behind the scenes” since the agent performs inference internally. In this work, we approach this issue from an economic perspective, by proposing a pay-for-performance, contract-based framework for incentivizing quality. We study a principal-agent game where the agent generates text using costly inference, and the contract determines the principal’s payment for the text according to an automated quality evaluation. Since standard contract theory is inapplicable when internal inference costs are unknown, we introduce cost-robust contracts. As our main theoretical contribution, we characterize optimal cost-robust contracts through a direct correspondence to optimal composite hypothesis tests from statistics, generalizing a result of Saig et al. (NeurIPS’23). We evaluate our framework empirically by deriving contracts for a range of objectives and LLM evaluation benchmarks, and find that cost-robust contracts sacrifice only a marginal increase in objective value compared to their cost-aware counterparts.
Dynamic Rescaling for Training GNNs
Authors: Nimrah Mustafa; Rebekka Burkholz
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Graph neural networks (GNNs) with a rescale invariance, such as GATs, can be re-parameterized during optimization through dynamic rescaling of network parameters and gradients while keeping the loss invariant. In this work, we explore dynamic rescaling as a tool to influence GNN training dynamics in two key ways: i) balancing the network with respect to various criteria, and ii) controlling the relative learning speeds of different layers. We gain novel insights, unique to GNNs, that reveal distinct training modes for different tasks. For heterophilic graphs, achieving balance based on relative gradients leads to faster training and better generalization. In contrast, homophilic graphs benefit from delaying the learning of later layers. Additionally, we show that training in balance supports larger learning rates, which can improve generalization. Moreover, controlling layer-wise training speeds is linked to grokking-like phenomena, which may be of independent interest.
The Fairness-Quality Tradeoff in Clustering
Authors: Rashida Hakim; Ana-Andreea Stoica; Christos Papadimitriou; Mihalis Yannakakis
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Fairness in clustering has been considered extensively in the past; however, the trade-off between the two objectives --- e.g., can we sacrifice just a little in the quality of the clustering to significantly increase fairness, or vice-versa? --- has rarely been addressed. We introduce novel algorithms for tracing the complete trade-off curve, or Pareto front, between quality and fairness in clustering problems; that is, computing all clusterings that are not dominated in both objectives by other clusterings. Unlike previous work that deals with specific objectives for quality and fairness, we deal with all objectives for fairness and quality in two general classes encompassing most of the special cases addressed in previous work. Our algorithm must take exponential time in the worst case as the Parero front itself can be exponential. Even when the Pareto front is polynomial, our algorithm may take exponential time, and we prove that this is inevitable unless P = NP. However, we also present a new polynomial-time algorithm for computing the entire Pareto front when the cluster centers are fixed, and for perhaps the most natural fairness objective: minimizing the sum, over all clusters, of the imbalance between the two groups in each cluster.