Advances in Personalization and Fairness of Large Language Models

The field of large language models (LLMs) is moving towards developing more personalized and fair models. Recent studies have highlighted the importance of aligning LLMs with individual preferences and universal human values. Personalized alignment techniques are being explored to enable LLMs to adapt their behavior within ethical boundaries based on individual preferences. Additionally, there is a growing focus on evaluating and mitigating biases in LLMs, particularly with regards to gender, race, and education.

Noteworthy papers include: A Survey on Personalized Alignment, which proposes a unified framework for personalized alignment and examines current techniques and potential risks. Personalized Language Models via Privacy-Preserving Evolutionary Model Merging presents a novel approach to personalization that employs gradient-free methods to optimize task-specific metrics while preserving user privacy. A Multilingual, Culture-First Approach to Addressing Misgendering in LLM Applications develops methodologies to assess and mitigate misgendering across multiple languages and dialects. The Greatest Good Benchmark introduces a benchmark to evaluate the moral judgments of LLMs using utilitarian dilemmas, revealing consistently encoded moral preferences that diverge from established moral theories.

Sources

Gender and content bias in Large Language Models: a case study on Google Gemini 2.0 Flash Experimental

A Survey on Personalized Alignment -- The Missing Piece for Large Language Models in Real-World Applications

Data to Decisions: A Computational Framework to Identify skill requirements from Advertorial Data

From Text to Talent: A Pipeline for Extracting Insights from Candidate Profiles

Personalized Language Models via Privacy-Preserving Evolutionary Model Merging

Evaluating Bias in LLMs for Job-Resume Matching: Gender, Race, and Education

The Greatest Good Benchmark: Measuring LLMs' Alignment with Utilitarian Moral Dilemmas

Poor Alignment and Steerability of Large Language Models: Evidence from College Admission Essays

A Multilingual, Culture-First Approach to Addressing Misgendering in LLM Applications

Embedding Domain-Specific Knowledge from LLMs into the Feature Engineering Pipeline

Agent-Centric Personalized Multiple Clustering with Multi-Modal LLMs

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