46 citations · 304 across the 59 of their papers we have counts for
68 papers
Enhancing LLM Planning Capabilities through Intrinsic Self-Critique
Bernd Bohnet, Pierre-Alexandre Kamienny, Hanie Sedghi +7
We demonstrate an approach for LLMs to critique their \emph{own} answers with the goal of enhancing their performance that leads to significant improvements over established planni…
The Limits of Preference Data for Post-Training
Eric Zhao, Jessica Dai, Pranjal Awasthi
Recent progress in strengthening the capabilities of large language models has stemmed from applying reinforcement learning to domains with automatically verifiable outcomes. A key…
From Style to Facts: Mapping the Boundaries of Knowledge Injection with Finetuning
Eric Zhao, Pranjal Awasthi, Nika Haghtalab
Finetuning provides a scalable and cost-effective means of customizing language models for specific tasks or response styles, with greater reliability than prompting or in-context…
Sample-Efficient Optimization over Generative Priors via Coarse Learnability
Pranjal Awasthi, Sreenivas Gollapudi, Ravi Kumar +1
We study zeroth-order optimization where solutions must minimize a cost while maintaining high probability under a complex generative prior (e.g., a parameterized mod…
Sample, Scrutinize and Scale: Effective Inference-Time Search by Scaling Verification
Eric Zhao, Pranjal Awasthi, Sreenivas Gollapudi
Sampling-based search, a simple paradigm for utilizing test-time compute, involves generating multiple candidate responses and selecting the best one -- typically by having models…
Learning Neural Networks with Sparse Activations
Pranjal Awasthi, Nishanth Dikkala, Pritish Kamath +1
A core component present in many successful neural network architectures, is an MLP block of two fully connected layers with a non-linear activation in between. An intriguing pheno…
ReMI: A Dataset for Reasoning with Multiple Images
Mehran Kazemi, Nishanth Dikkala, Ankit Anand +8
With the continuous advancement of large language models (LLMs), it is essential to create new benchmarks to effectively evaluate their expanding capabilities and identify areas fo…
Long-Span Question-Answering: Automatic Question Generation and QA-System Ranking via Side-by-Side Evaluation
Bernd Bohnet, Kevin Swersky, Rosanne Liu +9
We explore the use of long-context capabilities in large language models to create synthetic reading comprehension data from entire books. Previous efforts to construct such datase…
Position Coupling: Improving Length Generalization of Arithmetic Transformers Using Task Structure
Hanseul Cho, Jaeyoung Cha, Pranjal Awasthi +3
Even for simple arithmetic tasks like integer addition, it is challenging for Transformers to generalize to longer sequences than those encountered during training. To tackle this…
Stacking as Accelerated Gradient Descent
Naman Agarwal, Pranjal Awasthi, Satyen Kale +1
Stacking, a heuristic technique for training deep residual networks by progressively increasing the number of layers and initializing new layers by copying parameters from older la…
On Distributed Larger-Than-Memory Subset Selection With Pairwise Submodular Functions
Maximilian Böther, Abraham Sebastian, Pranjal Awasthi +2
Modern datasets span billions of samples, making training on all available data infeasible. Selecting a high quality subset helps in reducing training costs and enhancing model qua…
Majority Kernels: An Approach to Leverage Big Model Dynamics for Efficient Small Model Training
Hanna Mazzawi, Pranjal Awasthi, Xavi Gonzalvo +1
Recent breakthroughs and successful deployment of large language and vision models in a constrained environment predominantly follow a two phase approach. First, large models are t…
A Weighted K-Center Algorithm for Data Subset Selection
Srikumar Ramalingam, Pranjal Awasthi, Sanjiv Kumar
The success of deep learning hinges on enormous data and large models, which require labor-intensive annotations and heavy computation costs. Subset selection is a fundamental prob…
Improving Length-Generalization in Transformers via Task Hinting
Pranjal Awasthi, Anupam Gupta
It has been observed in recent years that transformers have problems with length generalization for certain types of reasoning and arithmetic tasks. In particular, the performance…
Best-Effort Adaptation
Pranjal Awasthi, Corinna Cortes, Mehryar Mohri
We study a problem of best-effort adaptation motivated by several applications and considerations, which consists of determining an accurate predictor for a target domain, for whic…
Congested Bandits: Optimal Routing via Short-term Resets
Pranjal Awasthi, Kush Bhatia, Sreenivas Gollapudi +1
For traffic routing platforms, the choice of which route to recommend to a user depends on the congestion on these routes -- indeed, an individual's utility depends on the number o…
Maximizing Use-Case Specificity through Precision Model Tuning
Pranjali Awasthi, David Recio-Mitter, Yosuke Kyle Sugi
Language models have become increasingly popular in recent years for tasks like information retrieval. As use-cases become oriented toward specific domains, fine-tuning becomes def…
On the Adversarial Robustness of Mixture of Experts
Joan Puigcerver, Rodolphe Jenatton, Carlos Riquelme +2
Adversarial robustness is a key desirable property of neural networks. It has been empirically shown to be affected by their sizes, with larger networks being typically more robust…
Agnostic Learning of General ReLU Activation Using Gradient Descent
Pranjal Awasthi, Alex Tang, Aravindan Vijayaraghavan
We provide a convergence analysis of gradient descent for the problem of agnostically learning a single ReLU function with moderate bias under Gaussian distributions. Unlike prior…
Individual Preference Stability for Clustering
Saba Ahmadi, Pranjal Awasthi, Samir Khuller +4
In this paper, we propose a natural notion of individual preference (IP) stability for clustering, which asks that every data point, on average, is closer to the points in its own…
Do More Negative Samples Necessarily Hurt in Contrastive Learning?
Pranjal Awasthi, Nishanth Dikkala, Pritish Kamath
Recent investigations in noise contrastive estimation suggest, both empirically as well as theoretically, that while having more "negative samples" in the contrastive loss improves…
Trimmed Maximum Likelihood Estimation for Robust Learning in Generalized Linear Models
Pranjal Awasthi, Abhimanyu Das, Weihao Kong +1
We study the problem of learning generalized linear models under adversarial corruptions. We analyze a classical heuristic called the iterative trimmed maximum likelihood estimator…
-Consistency Estimation Error of Surrogate Loss Minimizers
Pranjal Awasthi, Anqi Mao, Mehryar Mohri +1
We present a detailed study of estimation errors in terms of surrogate loss estimation errors. We refer to such guarantees as -consistency estimation error bounds, sin…
Distributionally Robust Data Join
Pranjal Awasthi, Christopher Jung, Jamie Morgenstern
Suppose we are given two datasets: a labeled dataset and unlabeled dataset which also has additional auxiliary features not present in the first dataset. What is the most principle…