Learning to Explore and Select for Coverage-Conditioned Retrieval-Augmented Generation

Interactions with billion-scale large language models typically yield long-form responses due to their extensive parametric capacities, along with retrieval-augmented features. While detailed responses provide insightful viewpoint of a specific subject, they frequently generate redundant and less engaging content that does not meet user interests. In this work, we focus on the role of query outlining (i.e., selected sequence of queries) in scenarios that users request a specific range of information, namely coverage-conditioned ($C^2$) scenarios. For simulating $C^2$ scenarios, we construct QTree, 10K sets of information-seeking queries decomposed with various perspectives on certain topics. By utilizing QTree, we train QPlanner, a 7B language model generating customized query outlines that follow coverage-conditioned queries. We analyze the effectiveness of generated outlines through automatic and human evaluation, targeting on retrieval-augmented generation (RAG). Moreover, the experimental results demonstrate that QPlanner with alignment training can further provide outlines satisfying diverse user interests. Our resources are available at https://github.com/youngerous/qtree.

Adversarial TableQA: Attention Supervision for Question Answering on Tables

The task of answering a question given a text passage has shown great developments on model performance thanks to community efforts in building useful datasets. Recently, there have been doubts whether such rapid progress has been based on truly understanding language. The same question has not been asked in the table question answering (TableQA) task, where we are tasked to answer a query given a table. We show that existing efforts, of using "answers" for both evaluation and supervision for TableQA, show deteriorating performances in adversarial settings of perturbations that do not affect the answer. This insight naturally motivates to develop new models that understand question and table more precisely. For this goal, we propose Neural Operator (NeOp), a multi-layer sequential network with attention supervision to answer the query given a table. NeOp uses multiple Selective Recurrent Units (SelRUs) to further help the interpretability of the answers of the model. Experiments show that the use of operand information to train the model significantly improves the performance and interpretability of TableQA models. NeOp outperforms all the previous models by a big margin.