Answering Questions by Meta-Reasoning over Multiple Chains of Thought (2023)

Abstract: Modern systems for multi-hop question answering (QA) typically break questions into a sequence of reasoning steps, termed chain-of-thought (CoT), before arriving at a final answer. Often, multiple chains are sampled and aggregated through a voting mechanism over the final answers, but the intermediate steps themselves are discarded. While such approaches improve performance, they do not consider the relations between intermediate steps across chains and do not provide a unified explanation for the predicted answer. We introduce Multi-Chain Reasoning (MCR), an approach which prompts large language models to meta-reason over multiple chains of thought, rather than aggregate their answers. MCR examines different reasoning chains, mixes information between them and selects the most relevant facts in generating an explanation and predicting the answer. MCR outperforms strong baselines on 7 multi-hop QA datasets. Moreover, our analysis reveals that MCR explanations exhibit high quality, enabling humans to verify its answers.

## introduction in chain-of-thought (cot) prompting, a large language model @xcite @xcite is prompted to generate its answer following a stepby-step explanation @xcite . cot prompting has been shown to dramatically improve performance on reasoning-heavy tasks @xcite . furthermore, @xcite showed that sampling multiple chains of thought and returning their majority output further improves accuracy, a method which they term self-consistency (sc). while sc leads to performance gains, it also has several shortcomings. first, when the space of possible outputs is large @xcite , each reasoning chain may lead to a different output, in figure 1 : an example from strategyqa, showing the output of multi-chain reasoning versus self-consistency. mcr uses reasoning chains as its context for qa. sc solely relies on the chains' answers. in this work, we propose multi-chain reasoning (mcr), where we prompt a large language model 3. meta-reason ## background recently, there has been a surge of interest in answering multi-hop questions through few-shot prompting of llms @xcite @xcite . the majority of these works follow a common standard: first, given a question, plan a step-by-step reasoning chain to derive the answer and solve all intermediate steps, aided by a retriever to minimize model hallucination @xcite @xcite @xcite . then, incorporate multiple reasoning chains with answers to derive the final answer @xcite . in our work, we follow this template and focus on the latter part. however, our meta-reasoning approach differs from prior work by reasoning on multiple reasoning chains. namely, we use multiple chains to collect relevant evidence for question answering. ## method we present a method for answering questions by meta-reasoning on multiple reasoning chains. our focus is on open-domain qa, where the input is a question q, and the evidence to answer it is found in one or more sentences in a corpus c. when answering q requires multiple reasoning steps, it can be expressed by a reasoning chain, denoted by r. the reasoning chain is a list of one or more intermediate question-evidence-answer triples (q i , e i , a i ). evidence e @xmath0. fig. 2 describes our approach when answering "how many ants would fit into the shard?". first, is it true that colonel walter phelps served the united states army for more than 30 years? q1: who is colonel walter phelps? e1: walter phelps: (oct 29, 1832-february 20, 1878) was an officer in the union army throughout the american civil war, serving as commanding officer of the eastern iron brigade. e1: walter phelps: (oct 29, 1832-february 20, 1878) was an officer… q1: who is colonel walter phelps? a1: colonel walter phelps was an officer in the union army throughout the american civil war. e1: walter phelps: (oct 29, 1832-february 20, 1878) was an officer… q1: who is colonel walter phelps? a1: colonel walter phelps was an officer in the union army throughout the american civil war. q2: how long did colonel walter phelps serve the united states army? e2: walter phelps: [['walter phelps jr.'], ['allegiance', 'united states of america union'], ['service/branch', 'united states army union army'], ['years of service', '1861-1865'], ['rank', 'colonel bvt. brigadier general']] e1: walter phelps: (oct 29, 1832-february 20, 1878) was an officer… e2: walter phelps: [['walter phelps jr.'] … q1: who is colonel walter phelps? a1: colonel walter … q2: how long did colonel walter phelps serve the united states army? a2: colonel walter phelps served the united states army for 4 years. retrieval step #1 decomposition ans. #1 decomposition step #1 decomposition step #2 we use a prompted llm to generate multiple reasoning chains, r (1) , ..., r (k) (steps 1-2). each r (j) is generated by interleaving generated intermediate questions with retrieved contexts ( §3.1). our main contribution is step 3: we introduce a second llm that is prompted to meta-reason on multiple reasoning chains, collecting evidence facts as its explanation and generating the final answer ( §3.2). ## experiments we compare mcr to existing methods on 7 multihop qa benchmarks. these cover a wide range of reasoning skills, including commonsense, composition, comparison and fact verification. mcr consistently outperforms existing approaches on all benchmarks, when experimenting with two different llms and retrievers. our setting is described in §4.1 and we discuss our main results in §4.2. ## analysis next, we measure the importance of incorporating multiple reasoning chains in mcr and qualitatively assess its output. when are multiple chains helpful? in §4.2 we observed that mcr consistently outperforms single-chain reasoning (scr). we wish to prove that this advantage lies in cases where the metareasoner uses additional chains. to this end, we sort examples based on the similarity of their greedydecoded chain to the mcr explanation (details in §c.1). lower similarity indicates less reliance of mcr on the greedy chain. fig. 6 presents an example where the mcr explanation (pink box) includes relevant facts from a chain other than the greedy one (additional examples in §c.2). results in fig. 7 empirically demonstrate that on strat-egyqa, mcr gains over scr are highest when mcr explanations are less similar to the greedy chain. we observe this trend in all datasets ( §c.1), serving as further evidence for mcr's strengths. combining reasoning chains in addition to choosing between reasoning chains, an interesting property of the meta-reasoner is that it can combine facts from different chains. we estimate the prevalence of this phenomenon on the implicit datasets, strategyqa and fermi, which are more challenging. given an example, we automati explanation quality the meta-reasoner is prompted to generate an explanation alongside the final answer ( §3.2). inspired by past work (pruthi et al., 2022), we test the quality of the mcr explanations. four of the authors manually reviewed 600 random examples, 100 per dataset (sans fever-ous §b.2) and scored their meta-reasoner explanations. each explanation is scored as either 1 (irrelevant), 2 (partially relevant) or 3 (highly relevant), based on its relevance to answering the question. we find the explanation is highly relevant in 82% of the cases (87% excluding fermi, which is the most challenging), and is irrelevant in less than 3%. next, we evaluate the faithfulness of explanations (jacovi and goldberg, 2020), namely, whether a person provided only with the question and mcr explanation would answer the same as the model. our focus was on examples with quality explanations (score 3), since they are answerable given the explanation. we answered each question based on the model's explanation. in 90% of cases (95% excluding fermi), the mcr predictions matched our own, highlighting the faithfulness of its explanations. we attribute part of the gap between human and mcr predictions to implicit reasoning tasks, where humans lead by five points, on average. for the full results, see §c.3. ## related work for a thorough survey on llm reasoning see @xcite ; huang and chang (2022); qiao et al. @xcite . a slew of recent works have focused on eliciting multi-step reasoning in llms, including scratchpads @xcite , chain-of-thought prompting @xcite , learned verifiers @xcite , selectioninference @xcite and bootstrapping @xcite . self-consistency @xcite selects the majority answer across multiple chains, outperforming learned verifiers and "sample-and-rank" approaches @xcite . li et al. (2022) further improve sc by increasing chains' diversity and introducing a trained verifier. tafjord et al. (2022) over-samples chains and verifies them using a natural language inference model on intermediate steps, while he et al. ( 2022 ) re-rank chains based on intermediate retrieved evidence. in addition, metareasoning is closely tied to self-reflection in llms, which is becoming increasingly important in using the llm to review multiple strategies @xcite @xcite . recent works proposed revising llm-generated texts by using retrieved sentences @xcite or model-generated feedback @xcite @xcite . mcr similarly reviews llm-generated reasoning chains however, its focus is meta-reasoning on multiple chains. significant qa research has been dedicated to reasoning over multiple facts retrieved from an underlying corpus. such tasks include multi-step questions that require explicit reasoning @xcite @xcite , implicit reasoning @xcite and multi-modal capabilities @xcite . recent works also target retrieval-augmented llms, prompted to solve open-domain questions @xcite @xcite @xcite . ## conclusion this work introduces mcr for meta-reasoning over multiple reasoning chains. we evaluate mcr on 7 datasets for multi-hop qa that require both implicit and explicit reasoning in an open-domain setting and show that it outperforms previous approaches on all evaluation benchmarks.