  We focus on a conversational question answering task which combines the
challenges of understanding questions in context and reasoning over evidence
gathered from heterogeneous sources like text, knowledge graphs, tables, and
infoboxes. Our method utilizes a graph structured representation to aggregate
information about a question and its context (i.e., the conversation so far and
evidence retrieved to find an answer), while also harnessing the reasoning and
text generation capabilities of large language models (LLMs). Graph embeddings
are directly injected into the LLM, bypassing the token embedding layers, and
learned end-to-end by minimizing cross-entropy. Our model maintains a memory
module to track and update past evidence, thus influencing the graph's
structure, as the conversation evolves. Experimental results on the ConvMix
benchmark(Christmann et al., 2022a) show that graph embeddings enhance the
LLM's ability to reason, while the memory module provides robustness against
noise and retrieval errors.
