Reward modeling in large language models is known to be susceptible to reward hacking, causing models to latch onto superficial features such as the tendency to generate lists or unnecessarily long responses. In RLHF, and more generally during post-training, flawed reward signals often lead to outputs that optimize for these spurious correlates instead of genuine quality or correctness. We propose **Carmo (Context-Aware Reward Modeling)**, a novel approach that first generates dynamic, context-relevant criteria to ground the reward model prior to producing reward scores. Unlike prior methods that use static rubrics, Carmo leverages powerful LLMs to adaptively create evaluation criteria, e.g., logical consistency, clarity, and depth, tailored to the user query. Our theoretical analysis shows that such criteria generation can mitigate reward hacking. We further demonstrate how Carmo can be distilled into smaller models, thereby lowering the computational cost of alignment. We establish a new state-of-the-art performance on zero shot setting for generative models, with a 2.1% improvement on Reward Bench. Furthermore, alignment performed on the Carmo-curated preference dataset achieves **22.5% and 21.1% LC-WR (%) and WR (%) on Mistral-Base (7B)**. We release our datasets at [huggingface/CARMO](https://huggingface.co/datasets/Multi-preference-Optimization/CARMO-UltraFeedback).

We study attribute control in language models through the method of Causal Average Treatment Effect (Causal ATE). Existing methodsfor the attribute control task in Language Models(LMs) check for the co-occurrence of words in a sentence with the attribute of interest, and control for them. However, spurious correlation of the words with the attribute in the training dataset, can cause models to hallucinate the presence of the attribute when presented with the spurious correlate during inference. We show that the simple perturbation-based method of Causal ATE removes this unintended effect. Specifically, we ground it in the problem of toxicity mitigation, where a significant challenge lies in the inadvertent bias that often emerges towards protected groups post detoxification. We show that this unintended bias can be solved by the use of the Causal ATE metric. We provide experimental validations for our claims and release our code (anonymously) here: [github.com/causalate-mitigates-bias](https://github.com/causalate-mitigates-bias/causal-ate-mitigates-bias).

We propose a method to control the attributes of Language Models (LMs) for the text generation task using Causal Average Treatment Effect (ATE) scores and counterfactual augmentation. We explore this method, in the context of LM detoxification, and propose the Causally Fair Language (CFL) architecture for detoxifying pre-trained LMs in a plug-and-play manner. Our architecture is based on a Structural Causal Model (SCM) that is mathematically transparent and computationally efficient as compared with many existing detoxification techniques. We also propose several new metrics that aim to better understand the behaviour of LMs in the context of toxic text generation. Further, we achieve state of the art performance for toxic degeneration, which are computed using Real Toxicity Prompts. Our experiments show that CFL achieves such a detoxification without much impact on the model perplexity. We also show that CFL mitigates the unintended bias problem through experiments on the BOLD dataset.