208 citations · 369 across the 38 of their papers we have counts for
41 papers
Activation-Conditioned Self-Distillation
Zhexi Lu, Subhajit Chaudhury, Tejaswini Pedapati +2
On-policy self-distillation uses a model as its own teacher to provide dense supervision for reasoning, often through reference-solution conditioning. Providing privileged informat…
BLINDSPOT: A Benchmark for Safety and Refusal Calibration in Long-Horizon Tool-Using Agents
Sadia Asif, Mohammad Mohammadi Amiri, Momin Abbas +2
Large language model (LLM) agents increasingly operate over long-horizon interactions involving tool use, persistent state, evolving authorization, and external environment feedbac…
Intermediate Representations are Strong AI-Generated Image Detectors
Zhenhan Huang, Pin-Yu Chen, Tejaswini Pedapati +1
The rapid advancement in generative AI models has enabled the creation of photorealistic images. At the same time, there are growing concerns about the potential misuse and dangers…
ZoomR: Memory Efficient Reasoning through Multi-Granularity Key Value Retrieval
David H. Yang, Yuxuan Zhu, Mohammad Mohammadi Amiri +4
Large language models (LLMs) have shown great performance on complex reasoning tasks but often require generating long intermediate thoughts before reaching a final answer. During…
AI Steerability 360: A Toolkit for Steering Large Language Models
Erik Miehling, Karthikeyan Natesan Ramamurthy, Praveen Venkateswaran +10
The AI Steerability 360 toolkit is an extensible, open-source Python library for steering LLMs. Steering abstractions are designed around four model control surfaces: input (modifi…
CoFrGeNet: Continued Fraction Architectures for Language Generation
Amit Dhurandhar, Vijil Chenthamarakshan, Dennis Wei +3
Transformers are arguably the preferred architecture for language generation. In this paper, inspired by continued fractions, we introduce a new function class for generative model…
Evaluating Joinable Column Discovery Approaches for Context-Aware Search
Harsha Kokel, Aamod Khatiwada, Tejaswini Pedapati +4
Joinable Column Discovery is a critical challenge in automating enterprise data analysis. While existing approaches focus on syntactic overlap and semantic similarity, there remain…
OjaKV: Context-Aware Online Low-Rank KV Cache Compression
Yuxuan Zhu, David H. Yang, Mohammad Mohammadi Amiri +3
The expanding long-context capabilities of large language models are constrained by a significant memory bottleneck: the key-value (KV) cache required for autoregressive generation…
TOPJoin: A Context-Aware Multi-Criteria Approach for Joinable Column Search
Harsha Kokel, Aamod Khatiwada, Tejaswini Pedapati +4
One of the major challenges in enterprise data analysis is the task of finding joinable tables that are conceptually related and provide meaningful insights. Traditionally, joinabl…
EvalAssist: A Human-Centered Tool for LLM-as-a-Judge
Zahra Ashktorab, Werner Geyer, Michael Desmond +6
With the broad availability of large language models and their ability to generate vast outputs using varied prompts and configurations, determining the best output for a given tas…
CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions
Isha Puri, Amit Dhurandhar, Tejaswini Pedapati +3
In recent years there has been a considerable amount of research on local post hoc explanations for neural networks. However, work on building interpretable neural architectures ha…
LongFuncEval: Measuring the effectiveness of long context models for function calling
Kiran Kate, Tejaswini Pedapati, Kinjal Basu +5
Multiple recent studies have documented large language models' (LLMs) performance on calling external tools/functions. Others focused on LLMs' abilities to handle longer context le…
EpMAN: Episodic Memory AttentioN for Generalizing to Longer Contexts
Subhajit Chaudhury, Payel Das, Sarathkrishna Swaminathan +6
Recent advances in Large Language Models (LLMs) have yielded impressive successes on many language tasks. However, efficient processing of long contexts using LLMs remains a signif…
Modular Prompt Learning Improves Vision-Language Models
Zhenhan Huang, Tejaswini Pedapati, Pin-Yu Chen +1
Pre-trained vision-language models are able to interpret visual concepts and language semantics. Prompt learning, a method of constructing prompts for text encoders or image encode…
STAR: Spectral Truncation and Rescale for Model Merging
Yu-Ang Lee, Ching-Yun Ko, Tejaswini Pedapati +3
Model merging is an efficient way of obtaining a multi-task model from several pretrained models without further fine-tuning, and it has gained attention in various domains, includ…
Sparse Gradient Compression for Fine-Tuning Large Language Models
David H. Yang, Mohammad Mohammadi Amiri, Tejaswini Pedapati +2
Fine-tuning large language models (LLMs) for downstream tasks has become increasingly crucial due to their widespread use and the growing availability of open-source models. Howeve…
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…