208 citations · 369 across the 38 of their papers we have counts for
9 papers · 1 filter
Differentiable Prompt Learning for Vision Language Models
Zhenhan Huang, Tejaswini Pedapati, Pin-Yu Chen +1
Prompt learning is an effective way to exploit the potential of large-scale pre-trained foundational models. Continuous prompts parameterize context tokens in prompts by turning th…
Granite Guardian
Inkit Padhi, Manish Nagireddy, Giandomenico Cornacchia +20
We introduce the Granite Guardian models, a suite of safeguards designed to provide risk detection for prompts and responses, enabling safe and responsible use in combination with…
Large Language Models can be Strong Self-Detoxifiers
Ching-Yun Ko, Pin-Yu Chen, Payel Das +6
Reducing the likelihood of generating harmful and toxic output is an essential task when aligning large language models (LLMs). Existing methods mainly rely on training an external…
Aligning Human and LLM Judgments: Insights from EvalAssist on Task-Specific Evaluations and AI-assisted Assessment Strategy Preferences
Zahra Ashktorab, Michael Desmond, Qian Pan +7
Evaluation of large language model (LLM) outputs requires users to make critical judgments about the best outputs across various configurations. This process is costly and takes ti…
TabSketchFM: Sketch-based Tabular Representation Learning for Data Discovery over Data Lakes
Aamod Khatiwada, Harsha Kokel, Ibrahim Abdelaziz +7
Enterprises have a growing need to identify relevant tables in data lakes; e.g. tables that are unionable, joinable, or subsets of each other. Tabular neural models can be helpful…
Large Language Model Confidence Estimation via Black-Box Access
Tejaswini Pedapati, Amit Dhurandhar, Soumya Ghosh +2
Estimating uncertainty or confidence in the responses of a model can be significant in evaluating trust not only in the responses, but also in the model as a whole. In this paper,…
Graph is all you need? Lightweight data-agnostic neural architecture search without training
Zhenhan Huang, Tejaswini Pedapati, Pin-Yu Chen +2
Neural architecture search (NAS) enables the automatic design of neural network models. However, training the candidates generated by the search algorithm for performance evaluatio…
NeuroPrune: A Neuro-inspired Topological Sparse Training Algorithm for Large Language Models
Amit Dhurandhar, Tejaswini Pedapati, Ronny Luss +4
Transformer-based Language Models have become ubiquitous in Natural Language Processing (NLP) due to their impressive performance on various tasks. However, expensive training as w…
From PEFT to DEFT: Parameter Efficient Finetuning for Reducing Activation Density in Transformers
Bharat Runwal, Tejaswini Pedapati, Pin-Yu Chen
Pretrained Language Models (PLMs) have become the de facto starting point for fine-tuning on downstream tasks. However, as model sizes continue to increase, traditional fine-tuning…