Getting M o RE out of Mixture of Language Model Reasoning Experts (2023)

Abstract: While recent large language models (LLMs) improve on various question answering (QA) datasets, it remains difficult for a single model to generalize across question types that require distinct reasoning abilities. We provide empirical evidence that state-of-the-art LLMs suffer from poor generalizability on reasoning types beyond those seen in the prompt. To remedy this, we propose a Mixture-of-Reasoning-Experts (MORE) framework that ensembles diverse specialized language models. We specialize the backbone language model with prompts optimized for different reasoning categories, including factual, multihop, mathematical, and commonsense reasoning. Our key insight is to leverage agreement among the specialized experts to select the best answer for each question, or to abstain from answering. This gives MORE higher accuracy than any single specialized model on a collection of 12 QA datasets from four reasoning types. Beyond generalizability, the interpretable design of MORE improves selective question answering results compared to baselines without incorporating inter-expert agreement. This framework is also more interpretable and useful to human consumers of QA outputs. Our human study confirms that presenting expert predictions and the answer selection process helps annotators more accurately calibrate when to trust the system’s output. We release all code and data to facilitate future work.

## introduction question answering (qa) is one of the most common interactions between humans and ai with a wide range of applications @xcite . when a qa system is deployed in-the-wild-where users can ask any question-the principal challenges are to handle the diversity of question types while ensuring reliability by only providing answers when the system has a high probability of 1 https://github.com/noviscl/more figure 1 : overview of more. in our more framework, each of the four specialized expert models produces a prediction for the test question and we train a classifier to select the best answer among them. the answer selector considers all the predictions and their confidence, as well as their agreement (e.g., in this example the factual expert makes the same prediction as the math expert and so this prediction gets a higher score). finally, if the selected answer's score is relatively low, the system abstains from answering. in this example, the correct answer should be 14, and more abstained correctly to avoid producing the wrong answer 13. toward these goals, one popular approach is to build a unified qa system. while general-purpose llms like gpt-3 (openai, 2022) demonstrate impressive question-answering abilities, they lack specialization on particular domains or reasoning types and often fall behind specialized models @xcite . moreover, to the public, these llms are massive black boxes: users have cannot connect the prediction process to whether the outputs are trustworthy. therefore, we go against this trend of building a single generalist language model, but rather design a more interpretable system that consists of a pool of specialized models and each question is answered by one of them. crucially, to best use complementary strengths of multiple qa models, we implement a pool of diverse and capable specialized models (e.g., by equipping llms with corresponding prompting strategies) for each specific reasoning type; then we train a classifier to select the best candidate answer from the specialized models for each question or to abstain from answering (figure 1 ). this framework, mixture-of-reasoning-experts (more), aims to both generalize and answer selectively. to obtain the most capable specialist models for each reasoning type, we leverage specialized prompting strategies such as chain-of-thought @xcite prompting and retrieval-augmented prompting. experiments on our collection of 12 qa datasets across four diverse reasoning types confirm that our specialist models outperform the backbone model without specialization, but they achieve much lower accuracy on question types outside of their expertise. with these specialized models, we propose our more framework to combine their strengths. more selects the best candidate answer from the pool of specialized models, and we teach more to abstain from answering if none of the candidate answers are correct. we design our answer selector based on these indicative features: (1) the match between the question type and each specialized model's expertise; (2) the confidence of each specialized model and the characteristics of their predictions; and (3) the agreement among all specialized models, which is a novel feature that we propose. experiments validate that by ensembling the specialized experts this way, more significantly outperforms any single specialized model across all four diverse reasoning types. apart from the improved generalizability of more, an important byproduct of cross-checking among specialized experts is to offer a useful signal for understanding the whole system's working mechanism. this is validated by the experimental results showing that incorporating agreement among different specialized experts leads to better selective qa results-where the system answers as many questions as possible while maintaining high accuracy-and presenting such internal decision processes to human annotators helps them determine the correctness of the system predictions more accurately and in a shorter time. ## problem setup given our goal of developing a qa system that generalizes across reasoning types and abstains appropriately, we introduce our task and evaluation details. ## mixture of reasoning experts this section introduces our mixture of reasoning experts (more) framework, including how to obtain diverse reasoning experts, how to ensemble them, and how to predict answer correctness. ## sanity check: more improves generalizability this section describes our experiments to verify that more's ensemble of diverse experts improves generalizability. ## more improves selective qa the previous section has confirmed the generalizability strength of more, but it is still far from perfect. in fact, it is impossible for any qa system to be perfectly accurate on all questions, thus highlighting the importance of abstention-the system should not output an answer when it is likely to be wrong. for this goal, more has the important advantage of being more interpretable since users can understand how the system derives the final answer by inspecting each expert's prediction and the answer selection process. we demonstrate the benefits of such interpretability via evaluation on automatic abstention as well as human abstention. ## conclusion we proposed the more framework where we construct a pool of specialized qa models that excel at different reasoning types, and then train an answer selector to select the best answer among them. experiments on 12 datasets covering four reasoning types demonstrate that more achieve better generalizability than all baselines. more importantly, the inter-expert agreement features in more offer useful signals for training effective calibrators that improve selective qa and also improve human verification of the system's final predictions. while we focused on prompting llms as specialized experts, the idea of combining the strengths of diverse experts can extend to any type of specialized models, even non-neural ones such as traditional information retrieval models, which is an interesting avenue for future work. additionally, future work could also explore other possible explanations to facilitate users' calibration and abstention, such as better explaining the strengths and weaknesses of individual specialized expert models. such efforts are especially important for highstakes settings that require careful fact-checking or verification of the system outputs @xcite .