Cover Image

Do the Right Thing, Just Debias! Multi-Category Bias Mitigation Using LLMs

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Roy, Amartya, Khanna, Danush, Mahapatra, Devanshu, Vasanthakumar, Das, Avirup, Ghosh, Kripabandhu
Format:	Preprint
Published:	2024
Subjects:	Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Legal Judgment Reimagined: PredEx and the Rise of Intelligent AI Interpretation in Indian Courts
by: Nigam, Shubham Kumar, et al.
Published: (2024)

Do Students Debias Like Teachers? On the Distillability of Bias Mitigation Methods
by: Cheng, Jiali, et al.
Published: (2025)

Causal Reasoning Favors Encoders: On The Limits of Decoder-Only Models
by: Roy, Amartya, et al.
Published: (2025)

SELF-PERCEPT: Introspection Improves Large Language Models' Detection of Multi-Person Mental Manipulation in Conversations
by: Khanna, Danush, et al.
Published: (2025)

SMITE: Enhancing Fairness in LLMs through Optimal In-Context Example Selection via Dynamic Validation
by: Chhikara, Garima, et al.
Published: (2025)

Code-Mixer Ya Nahi: Novel Approaches to Measuring Multilingual LLMs' Code-Mixing Capabilities
by: Gupta, Ayushman, et al.
Published: (2024)

Mapping Clinical Doubt: Locating Linguistic Uncertainty in LLMs
by: Sridhar, Srivarshinee, et al.
Published: (2025)

Beyond Performance: Quantifying and Mitigating Label Bias in LLMs
by: Reif, Yuval, et al.
Published: (2024)

Don't Judge a Book by its Cover: Testing LLMs' Robustness Under Logical Obfuscation
by: Borah, Abhilekh, et al.
Published: (2026)

QuickSilver -- Speeding up LLM Inference through Dynamic Token Halting, KV Skipping, Contextual Token Fusion, and Adaptive Matryoshka Quantization
by: Khanna, Danush, et al.
Published: (2025)

Applicability of Large Language Models and Generative Models for Legal Case Judgement Summarization
by: Deroy, Aniket, et al.
Published: (2024)

AdversariaL attacK sAfety aLIgnment(ALKALI): Safeguarding LLMs through GRACE: Geometric Representation-Aware Contrastive Enhancement- Introducing Adversarial Vulnerability Quality Index (AVQI)
by: Khanna, Danush, et al.
Published: (2025)

ReGal: A First Look at PPO-based Legal AI for Judgment Prediction and Summarization in India
by: Nigam, Shubham Kumar, et al.
Published: (2025)

Why LLMs Fail at Causal Discovery and How Interventional Agents Escape
by: Roy, Amartya, et al.
Published: (2026)

Multilingual Controlled Generation And Gold-Standard-Agnostic Evaluation of Code-Mixed Sentences
by: Gupta, Ayushman, et al.
Published: (2024)

MARRO: Multi-headed Attention for Rhetorical Role Labeling in Legal Documents
by: Bambroo, Purbid, et al.
Published: (2025)

Few-Shot Fairness: Unveiling LLM's Potential for Fairness-Aware Classification
by: Chhikara, Garima, et al.
Published: (2024)

Disclosure and Mitigation of Gender Bias in LLMs
by: Dong, Xiangjue, et al.
Published: (2024)

Personalisation or Prejudice? Addressing Geographic Bias in Hate Speech Detection using Debias Tuning in Large Language Models
by: Piot, Paloma, et al.
Published: (2025)

Do Multilingual Large Language Models Mitigate Stereotype Bias?
by: Nie, Shangrui, et al.
Published: (2024)

HateDebias: On the Diversity and Variability of Hate Speech Debiasing
by: Wu, Hongyan, et al.
Published: (2024)

Can AI Debias the News? LLM Interventions Improve Cross-Partisan Receptivity but LLMs Overestimate Their Own Effectiveness
by: Feroz, Faisal, et al.
Published: (2026)

The power of Prompts: Evaluating and Mitigating Gender Bias in MT with LLMs
by: Sant, Aleix, et al.
Published: (2024)

"Yeah Right!" -- Do LLMs Exhibit Multimodal Feature Transfer?
by: Reichman, Benjamin, et al.
Published: (2025)

Self-Debias: Self-correcting for Debiasing Large Language Models
by: Feng, Xuan, et al.
Published: (2026)

Can We Trust LLMs? Mitigate Overconfidence Bias in LLMs through Knowledge Transfer
by: Yang, Haoyan, et al.
Published: (2024)

On the effective transfer of knowledge from English to Hindi Wikipedia
by: Das, Paramita, et al.
Published: (2024)

Steering Towards Fairness: Mitigating Political Bias in LLMs
by: Nadeem, Afrozah, et al.
Published: (2025)

RADIANT: Retrieval AugmenteD entIty-context AligNmenT -- Introducing RAG-ability and Entity-Context Divergence
by: Rawte, Vipula, et al.
Published: (2025)

Bias Mitigation or Cultural Commonsense? Evaluating LLMs with a Japanese Dataset
by: Yamamoto, Taisei, et al.
Published: (2025)

Understanding and Mitigating Gender Bias in LLMs via Interpretable Neuron Editing
by: Yu, Zeping, et al.
Published: (2025)

LaMSUM: Amplifying Voices Against Harassment through LLM Guided Extractive Summarization of User Incident Reports
by: Chhikara, Garima, et al.
Published: (2024)

DPO Kernels: A Semantically-Aware, Kernel-Enhanced, and Divergence-Rich Paradigm for Direct Preference Optimization
by: Das, Amitava, et al.
Published: (2025)

LLMs Are Biased Towards Output Formats! Systematically Evaluating and Mitigating Output Format Bias of LLMs
by: Long, Do Xuan, et al.
Published: (2024)

Backdoor for Debias: Mitigating Model Bias with Backdoor Attack-based Artificial Bias
by: Wu, Shangxi, et al.
Published: (2023)

Too Helpful, Too Harmless, Too Honest or Just Right?
by: Kashyap, Gautam Siddharth, et al.
Published: (2025)

Mitigating Gender Bias via Fostering Exploratory Thinking in LLMs
by: Wei, Kangda, et al.
Published: (2025)

Empathy and the Right to Be an Exception: What LLMs Can and Cannot Do
by: Kidder, William, et al.
Published: (2024)

Do Prevalent Bias Metrics Capture Allocational Harms from LLMs?
by: Cyberey, Hannah, et al.
Published: (2024)

CalibraEval: Calibrating Prediction Distribution to Mitigate Selection Bias in LLMs-as-Judges
by: Li, Haitao, et al.
Published: (2024)