Momentum Posterior Regularization for Multi-hop Dense Retrieval (2025.coling-main)
Copied to clipboard
| Challenge: | Current methods for knowledge distillation in one-time retrieval are ineffective for multi-hop QA . posterior information is often defined as the response, which may not connect to the query without intermediate retrieval . |
| Approach: | They propose to distill knowledge from a posterior retrieval into a prior retrieval for multi-hop QA . they propose to use momentum moving average method to update posterior information along with prior retrievals . |
| Outcome: | Experiments on HotpotQA and StrategyQA show that MoPo outperforms baselines in retrieval and downstream QA tasks. |
Similar Papers
End-to-End Beam Retrieval for Multi-Hop Question Answering (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing beam retrieval frameworks for multi-hop question answering were customized for two-hop questions and were poorly supervised. |
| Approach: | They propose an end-to-end beam retrieval framework for multi-hop question answering . they combine an encoder and two classification heads to optimize the retrieval process . |
| Outcome: | The proposed framework improves on MuSiQue-Ans and surpasses all previous retrievers on HotpotQA and achieves 99.9% precision on 2WikiMultiHopQA. |
GRITHopper: Decomposition-Free Multi-Hop Dense Retrieval (2026.eacl-long)
Copied to clipboard
| Challenge: | Decomposition-based multi-hop retrieval methods rely on autoregressive steps to break down complex queries, which breaks end-to-end differentiability and is computationally expensive. |
| Approach: | They propose a multi-hop dense retrieval model that integrates causal language modeling with dense retrievals. |
| Outcome: | The proposed model outperforms existing methods on in-distribution and out-of-difference benchmarks. |
StepKE: Stepwise Knowledge Editing for Multi-Hop Question Answering (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing knowledge editing methods overlook interplay with pre-existing knowledge, leading to inconsistent edit propagation. |
| Approach: | stepKE integrates edited and existing knowledge for coherent multi-hop reasoning . stepKE decomposes multi-step questions into sequential single-hop sub-questions . |
| Outcome: | Experiments show that StepKE generates more accurate and consistent responses than baselines. |
Commonsense for Generative Multi-Hop Question Answering Tasks (D18-1)
Copied to clipboard
| Challenge: | Reading comprehension QA tasks have seen a recent surge in popularity, yet most work has focused on fact-finding extractive QA. |
| Approach: | They propose a multi-hop generative task that uses a pointer-generator decoder to synthesize disjoint pieces of information within the context to generate an answer. |
| Outcome: | The proposed model performs better than previous generative models and is competitive with current state-of-the-art span prediction models. |
Simple yet Effective Bridge Reasoning for Open-Domain Multi-Hop Question Answering (D19-58)
Copied to clipboard
| Challenge: | Existing work on open-domain multi-hop question answering relies on off-the-shelf information retrieval techniques to retrieve answer passages. |
| Approach: | They propose a new subproblem for open-domain multi-hop question answering . they aim to recognize the anchor from a set of start passages with a reading comprehension model . |
| Outcome: | The proposed method significantly improves the baseline method on the open-domain hotpotQA benchmark. |
Weakly Supervised Pre-Training for Multi-Hop Retriever (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for weakly supervised multi-hop pretraining require costly human annotation. |
| Approach: | They propose a method for weakly supervised multi-hop retriever pretraining without human efforts by generating vector representations of complex questions and subquestion as weak supervision for pre-training. |
| Outcome: | The proposed method is effective and robust on limited data and computational resources. |
A Sequential Flow Control Framework for Multi-hop Knowledge Base Question Answering (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for multi-hop reasoning in knowledge base question answering are coarse-grained and may bring information loss. |
| Approach: | They propose a sequential reasoning self-attention mechanism to capture the crucial reasoning information of each hop in a more fine-grained way. |
| Outcome: | The proposed model achieves new state-of-the-art Hits@1 of 76.8% on WebQSP and is also effective when KB is incomplete. |
Generative Context Pair Selection for Multi-hop Question Answering (2021.emnlp-main)
Copied to clipboard
Dheeru Dua, Cicero Nogueira dos Santos, Patrick Ng, Ben Athiwaratkun, Bing Xiang, Matt Gardner, Sameer Singh
| Challenge: | Recent studies have shown that discriminative training results in models that exploit these underlying biases to achieve a better held-out performance, without learning the right way to reason. |
| Approach: | They propose a generative context selection model for multi-hop QA that reasons about how the given question could have been generated given a context pair and not just independent contexts. |
| Outcome: | The proposed model outperforms the state-of-the-art model on hotpotQA while being comparable to the state of the art answering performance on adversarial held-out set. |
If You Want to Go Far Go Together: Unsupervised Joint Candidate Evidence Retrieval for Multi-hop Question Answering (2021.naacl-main)
Copied to clipboard
| Challenge: | et al. : evidence retrieval is highly dependent on partial, incorrect or no supporting knowledge. |
| Approach: | They propose a method that retrieves and reranks evidence facts jointly . they propose to account for links between sentences and coverage with the given query . |
| Outcome: | The proposed approach achieves state-of-the-art evidence retrieval performance on two multi-hop question answering datasets. |
Uncertainty Guided Global Memory Improves Multi-Hop Question Answering (2023.emnlp-main)
Copied to clipboard
| Challenge: | Transformers are used to solve multi-hop question answering tasks that require reasoning over multiple parts of a long document. |
| Approach: | They propose a method that collects relevant information over the entire document and then combines it with local context to solve a multi-hop question answering task. |
| Outcome: | The proposed method improves on three MHQA datasets compared to the baseline model. |