468 citations · 558 across the 22 of their papers we have counts for
7 papers · 1 filter
Change Is the Only Constant: Dynamic LLM Slicing based on Layer Redundancy
Razvan-Gabriel Dumitru, Paul-Ioan Clotan, Vikas Yadav +2
This paper introduces a novel model compression approach through dynamic layer-specific pruning in Large Language Models (LLMs), enhancing the traditional methodology established b…
Do LLMs Know When to NOT Answer? Investigating Abstention Abilities of Large Language Models
Nishanth Madhusudhan, Sathwik Tejaswi Madhusudhan, Vikas Yadav +1
Abstention Ability (AA) is a critical aspect of Large Language Model (LLM) reliability, referring to an LLM's capability to withhold responses when uncertain or lacking a definitiv…
Explicit Diversity Conditions for Effective Question Answer Generation with Large Language Models
Vikas Yadav, Hyuk Joon Kwon, Vijay Srinivasan +1
Question Answer Generation (QAG) is an effective data augmentation technique to improve the accuracy of question answering systems, especially in low-resource domains. While recent…
Paraphrase and Aggregate with Large Language Models for Minimizing Intent Classification Errors
Vikas Yadav, Zheng Tang, Vijay Srinivasan
Large language models (LLM) have achieved remarkable success in natural language generation but lesser focus has been given to their applicability in decision making tasks such as…
Layer-Wise Quantization: A Pragmatic and Effective Method for Quantizing LLMs Beyond Integer Bit-Levels
Razvan-Gabriel Dumitru, Vikas Yadav, Rishabh Maheshwary +3
We present a simple meta quantization approach that quantizes different layers of a large language model (LLM) at different bit levels, and is independent of the underlying quantiz…
M2Lingual: Enhancing Multilingual, Multi-Turn Instruction Alignment in Large Language Models
Rishabh Maheshwary, Vikas Yadav, Hoang Nguyen +2
Instruction finetuning (IFT) is critical for aligning Large Language Models (LLMs) to follow instructions. While many effective IFT datasets have been introduced recently, they pre…
Curry-DPO: Enhancing Alignment using Curriculum Learning & Ranked Preferences
Pulkit Pattnaik, Rishabh Maheshwary, Kelechi Ogueji +2
Direct Preference Optimization (DPO) is an effective technique that leverages pairwise preference data (usually one chosen and rejected response pair per user prompt) to align LLMs…