37 citations · 251 across the 96 of their papers we have counts for
4 papers · 1 filter
Quantifying and Mitigating Selection Bias in LLMs: A Transferable LoRA Fine-Tuning and Efficient Majority Voting Approach
Blessed Guda, Lawrence Francis, Gabrial Zencha Ashungafac +2
Multiple Choice Question (MCQ) answering is a widely used method for evaluating the performance of Large Language Models (LLMs). However, LLMs often exhibit selection bias in MCQ t…
Neural Bandit Based Optimal LLM Selection for a Pipeline of Subtasks
Baran Atalar, Eddie Zhang, Carlee Joe-Wong
As large language models (LLMs) become increasingly popular, there is a growing need to predict which out of a set of LLMs will yield a successful answer to a given query at low co…
QMOS: Enhancing LLMs for Telecommunication with Question Masked loss and Option Shuffling
Blessed Guda, Gabrial Zencha Ashungafac, Lawrence Francis +1
Large Language models (LLMs) have brought about substantial advancements in the field of Question Answering (QA) systems. These models do remarkably well in addressing intricate in…
Efficient Contextual LLM Cascades through Budget-Constrained Policy Learning
Xuechen Zhang, Zijian Huang, Ege Onur Taga +3
Recent successes in natural language processing have led to the proliferation of large language models (LLMs) by multiple providers. Each LLM offering has different inference accur…