Faithful Question Answering with M onte- C arlo Planning (2023)

Abstract: Although large language models demonstrate remarkable question-answering performances, revealing the intermediate reasoning steps that the models faithfully follow remains challenging. In this paper, we propose FAME (FAithful question answering with MontE-carlo planning) to answer questions based on faithful reasoning steps. The reasoning steps are organized as a structured entailment tree, which shows how premises are used to produce intermediate conclusions that can prove the correctness of the answer. We formulate the task as a discrete decision-making problem and solve it through the interaction of a reasoning environment and a controller. The environment is modular and contains several basic task-oriented modules, while the controller proposes actions to assemble the modules. Since the search space could be large, we introduce a Monte-Carlo planning algorithm to do a look-ahead search and select actions that will eventually lead to high-quality steps. FAME achieves advanced performance on the standard benchmark. It can produce valid and faithful reasoning steps compared with large language models with a much smaller model size.

## introduction enabling machines to reason and answer questions is a long-term pursuit in the ai community @xcite . in the field of questionanswering (qa), large language models (llms) have achieved strong performances @xcite @xcite . however, the intermediate reasoning steps from the known premises to the answer are often implicit and invisible. while some approaches encourage llms to produce the reasoning steps explicitly before generating the answer @xcite ), the answer may not faithfully follow the intermediate steps, i.e., the model could generate irrelevant or invalid steps while still resulting in the correct ## task definition faithful question-answering @xcite ) requires to answer the question and provide the valid reasoning steps that the answer faithfully follows. the inputs include a question q and candidate options @xmath0, o 2 , . . . , o |o| )}. 1 the desired outputs are valid reasoning steps t in the form of the entailment tree and an answer o @xmath1, which follows t . the entailment tree t consists of multi-premise entailment steps, whose leaf nodes (sent * ) are facts selected from a fact corpus c, intermediate nodes (int * ) are novel intermediate conclusions. a tree is considered valid if each non-leaf node is a valid entailment of its immediate children, and is considered faithful if its conclusion of the root node can support the option o a . ## our approach: fame given a question q and options o, following previous works @xcite , we first convert them into declarative hypotheses {h 1 , . . . , h |o| }. 2 we then try to generate an entailment tree for each hypothesis in a forward chaining manner and select the most plausible option based on the validity and faithfulness of trees. we propose to formulate the task as a discrete decision-making problem. the reasoning is done through several interactions between a reasoning environment and a controller. in each interaction, the controller observes a state from the environment and predicts the next action. then the environment executes the action and updates its state. 3 since the search space could be large, we introduce a monte-carlo planning algorithm to select the optimal action. we introduce details about the environment, controller, and monte-carlo planning in sec 4.1, 4.2, and 4.3, respectively. ## conclusion we propose fame for faithful question answering. it formulates the task as a discrete decision-making problem and tackles it with a modular reasoning environment and a controller. fame uses the monte-carlo planning to make more informed and foresighted decisions. experiments show that fame can answer questions based on valid and faithful steps and achieve advanced performance.