With the proliferation of LLMs, Salesforce has continued to be at the forefront of research in trusted AI. The main focus of our contributions, and the focus of this blog post, have been on factual correctness and evaluation; for AI methods to be trusted the output must be factually consistent with the input and evaluation must be robust to understand the strengths and weaknesses of the models. Below we briefly describe some of our contributions along the dimensions of improving methods for factual consistency along with our work on benchmarking and evaluation.
Within improving factual consistency, some of our work has afghanistan phone number list focused on better grounding entities found in the input context and ensembling models trained on varying levels of noisy data in our CaPE paper. In our Socratic pretraining paper, we proposed better ways to pretrain a model that allows for the grounding of the output on important questions that a user may ask while also making the model more controllable. Where training a model further can be difficult, we have proposed several methods that edit an existing model’s output by either compressing the output or verifying the model’s reasoning.
Methods for evaluation
In order to fully understand the improvements introduced by the methods discussed above, a thorough evaluation is essential. We have introduced automatic methods for to assess whether a model’s summary is factual consistent with its input context. Our approaches include a model that checks whether the summary is entailed by the input and a model that verifies whether the input context can be used to answer questions based on the model summary. However, much of our work focuses on building benchmarks that aim to understand the current status of the field. These benchmarks cover a diverse set of tasks, from fact-checking in dialogues to classification in multi-turn settings to analyze whether a model flip-flops, or changes its answers to queries.