Fixing R2FM paper title

_publications/calanzone2024locolm.md

@@ -2,7 +2,7 @@
 collection: publications
 ref: "calanzone2024locolm"
 permalink: "publications/calanzone2024locolm"
-title:  "Galerkin meets Laplace: Fast uncertainty estimation in neural PDEs"
+title:  "Towards Logically Consistent Language Models via Probabilistic Reasoning"
 date:   2024-03-05 10:00
 tags: nesy probml llm
 image: "/images/papers/calanzone2024locolm/locolm.png"
@@ -13,7 +13,7 @@ venue: "R2FM Workshop @ ICLR 2024"
 excerpt: "We introduce a training objective based on principled probabilistic reasoning that teaches a LLM to be logically consistent with a set of external facts and rules, allowing to extrapolate to unseen but semantically similar factual knowledge."
 abstract: "Large language models (LLMs) are a promising venue for natural language understanding and generation tasks. However, current LLMs are far from reliable: they are prone to generate non-factual information and, more crucially, to contradict themselves when prompted to reason about beliefs of the world. These problems are currently addressed with large scale fine-tuning or by delegating consistent reasoning to external tools. In this work, we strive for a middle ground and introduce a training objective based on principled probabilistic reasoning that teaches a LLM to be consistent with external knowledge in the form of a set of facts and rules. Fine-tuning with our loss on a limited set of facts enables our LLMs to be more logically consistent than previous baselines and allows them to extrapolate to unseen but semantically similar factual knowledge more systematically."
 bibtex: "@inproceedings{calanzone2024locolm,
-  title={Galerkin meets Laplace: Fast uncertainty estimation in neural PDEs},
+  title={Towards Logically Consistent Language Models via Probabilistic Reasoning},
   author={Diego Calanzone, Antonio Vergari, Stefano Teso},
   booktitle={ICLR 2024 Workshop on Reliable and Responsible Foundation Models},
   year={2024}