AMBEDKAR-A Multi-level Bias Elimination through a Decoding Approach with
  Knowledge Augmentation for Robust Constitutional Alignment of Language Models
Large Language Models (LLMs) can inadvertently reflect societal biases
present in their training data, leading to harmful or prejudiced outputs. In
the Indian context, our empirical evaluations across a suite of models reveal
that biases around caste and religion are particularly salient. Yet, most
existing mitigation strategies are Western-centric and fail to address these
local nuances. We propose AMBEDKAR, a framework inspired by the egalitarian
vision of Dr B. R. Ambedkar, architect of the Indian Constitution, to guide LLM
outputs toward fairness, neutrality, and inclusion in line with Articles 14 to
17. Our approach introduces a Constitution-Aware Decoding Layer, guided by the
AI Constitution of India and applied only at inference time, without any
parameter updates to the base model. We incorporate a speculative decoding
algorithm that proactively reduces casteist and communal bias during
generation. This mitigation layer operates directly within the decoding
process, avoiding changes to model internals and lowering the computational and
infrastructural costs associated with retraining. We reinterpret speculative
decoding not merely as an efficiency tool but as a mechanism for fairness. In
this framework, a Small Language Model (SLM) acts as a potentially biased
generator, while a constitutionally guided Large Language Model (LLM) serves as
the verifier. Rather than accelerating generation, the LLM enforces bias-robust
trajectories in the SLM outputs. This inversion of roles gives rise to a
fairness-by-speculation paradigm. Our approach yields an absolute reduction of
bias up to 26.41 percent compared to baseline. Our source code, datasets, and
results are available at https://anonymous.4open.science/r/AMBEDKAR-983B/
68b7e5ae295f15ff609114e0
Snehasis Mukhopadhyay
68b7e5ae295f15ff609114e1
Aryan Kasat
68b7e5ae295f15ff609114e2
Shivam Dubey
68b7e5ae295f15ff609114e3
Rahul Karthikeyan
68b7e5ae295f15ff609114e4
Dhruv Sood
68b7e5ae295f15ff609114e5
Vinija Jain
68b7e5ae295f15ff609114e6
Aman Chadha
claimed_verified
2025-09-03T08:25:56.162Z
63a4754927f1f64ed7238dac
https://cdn-avatars.huggingface.co/v1/production/uploads/63a4754927f1f64ed7238dac/aH-eJF-31g4vof9jv2gmI.jpeg
Aman Chadha
user
amanchadha
68b7e5ae295f15ff609114e7
Amitava Das
AMBEDKAR-A Multi-level Bias Elimination through a Decoding Approach with
  Knowledge Augmentation for Robust Constitutional Alignment of Language Models
Large Language Models (LLMs) can inadvertently reflect societal biases
present in their training data, leading to harmful or prejudiced outputs. In
the Indian context, our empirical evaluations across a suite of models reveal
that biases around caste and religion are particularly salient. Yet, most
existing mitigation strategies are Western-centric and fail to address these
local nuances. We propose AMBEDKAR, a framework inspired by the egalitarian
vision of Dr B. R. Ambedkar, architect of the Indian Constitution, to guide LLM
outputs toward fairness, neutrality, and inclusion in line with Articles 14 to
17. Our approach introduces a Constitution-Aware Decoding Layer, guided by the
AI Constitution of India and applied only at inference time, without any
parameter updates to the base model. We incorporate a speculative decoding
algorithm that proactively reduces casteist and communal bias during
generation. This mitigation layer operates directly within the decoding
process, avoiding changes to model internals and lowering the computational and
infrastructural costs associated with retraining. We reinterpret speculative
decoding not merely as an efficiency tool but as a mechanism for fairness. In
this framework, a Small Language Model (SLM) acts as a potentially biased
generator, while a constitutionally guided Large Language Model (LLM) serves as
the verifier. Rather than accelerating generation, the LLM enforces bias-robust
trajectories in the SLM outputs. This inversion of roles gives rise to a
fairness-by-speculation paradigm. Our approach yields an absolute reduction of
bias up to 26.41 percent compared to baseline. Our source code, datasets, and
results are available at https://anonymous.4open.science/r/AMBEDKAR-983B/
Ambekar framework uses a Constitution-Aware Decoding Layer to mitigate caste and religious biases in LLM outputs through speculative decoding without retraining.
Large Language Models LLMs Small Language Model SLM Constitution-Aware Decoding Layer speculative decoding fairness neutrality inclusion bias mitigation AI Constitution of India fairness-by-speculation