Enhancing large language models: toward more reliable and equitable NLP
Adiga, Rishabh
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https://hdl.handle.net/2142/129259
Description
Title
Enhancing large language models: toward more reliable and equitable NLP
Author(s)
Adiga, Rishabh
Issue Date
2025-04-28
Director of Research (if dissertation) or Advisor (if thesis)
Chandrasekaran, Varun
Department of Study
Siebel School Comp & Data Sci
Discipline
Computer Science
Degree Granting Institution
University of Illinois Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
LLM
Few-shot
Attention
Bias
Prompting
Fairness
Metrics
Localization
Mitigation
Language
eng
Abstract
Recent advances in large language models (LLMs) have enabled them to achieve striking fewshot performance on complex tasks, yet they can remain sensitive to prompt configurations and prone to subtle biases. This thesis addresses two central problems: (1) selecting informative few-shot examples and (2) localizing and mitigating bias in ambiguous comparative prompts. First, we propose a complexity-based approach for selecting examples in few-shot sequence tagging tasks, aiming to align test examples with training examples based on syntactic and semantic metrics. By focusing on features like sentence similarity, length matching, and label diversity, we achieve more consistent and robust outcomes without fine-tuning or adding parameters. Second, we develop a method to localize and mitigate bias in LLMs by examining the attention layers that favor certain entities over others. We then scale attention in those identified layers to reduce skewed preferences, preserving overall model fluency while mitigating biases. Extensive evaluations confirm that this targeted attention manipulation provides a lightweight way to address fairness concerns without sacrificing downstream accuracy.
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