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README.md

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 # Aligning Language Models with Observational Data
 
-Official repository of the [paper](https://arxiv.org/abs/2506.01844) **"Aligning Language Models with Observational Data: Opportunities and Risks from a Causal Perspective"**. The project explores methods to fine-tune large language models (LLMs) using observational data, tackling challenges like spurious correlations and confounding. We propose **DECONFOUNDLM**, a novel approach to mitigate these issues by correcting for known confounders in the reward signals.
+Official repository of the [paper](https://arxiv.org/abs/2506.00152) **"Aligning Language Models with Observational Data: Opportunities and Risks from a Causal Perspective"**. The project explores methods to fine-tune large language models (LLMs) using observational data, tackling challenges like spurious correlations and confounding. We propose **DECONFOUNDLM**, a novel approach to mitigate these issues by correcting for known confounders in the reward signals.
 
 # Code to be released soon.
 You can find the project webpage at [deconfoundlm.github.io](https://deconfoundlm.github.io).