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.

我们提出了一种在对话中理解问题并从不同来源（如文本、知识图谱、表格和信息框）中进行推理的说话式问答任务，我们的方法利用了图结构化表示，通过最小化交叉熵直接将图嵌入到大型语言模型中，同时利用了大型语言模型的推理和文本生成能力。我们的模型通过维护一个存储模块来追踪和更新过去的证据，从而影响图的结构，并在ConvMix基准测试中取得了实验结果，证明了图嵌入增强了大型语言模型的推理能力，而存储模块则提供了对噪声和检索错误的鲁棒性。

将大型语言模型与基于图的推理相结合，用于对话式问答