Chain-of-Discussion: A Multi-Model Framework for Complex Evidence-Based Question Answering (2025.coling-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable language generation capabilities, propelling advancements in various understanding/generation tasks, including opendomain question answering (QA). |
| Approach: | They propose a chain-of- Discussion framework to leverage synergy among multiple open-source Large Language Models (LLMs) aiming to provide more correct and more comprehensive answers for open-ended QA, although they are not strong enough individually. |
| Outcome: | The proposed framework leverages the synergy among multiple open-source Large Language Models (LLMs) to provide more correct and comprehensive answers for open-ended QA, although they are not strong enough individually. |
Similar Papers
Open-domain Question Answering via Chain of Reasoning over Heterogeneous Knowledge (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing open-domain question answering methods rely on the retriever to gather all evidence in isolation, but our approach uses an intermediary module to perform a chain of reasoning over the retrieved set. |
| Approach: | They propose a new open-domain question answering framework that integrates an intermediary module into the current retriever-reader pipeline and integrates it into the model. |
| Outcome: | The proposed framework outperforms the state-of-the-art on two OTT-QA datasets with an exact match score of 47.3 (45% relative gain). |
DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text (2024.findings-naacl)
Copied to clipboard
| Challenge: | Retrievalaugmented LLMs have been used to ground LLM in external knowledge . a gap exists in the current landscape regarding the effectiveness of grounding LLM on heterogeneous knowledge sources. |
| Approach: | They propose a model that uses symbolic language to generate symbolic queries . they use a dataset that is generated using predefined reasoning chains and human annotation . |
| Outcome: | The proposed model outperforms previous approaches by a significant margin in QA tasks over text. |
Leveraging Structured Information for Explainable Multi-hop Question Answering and Reasoning (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Neural models, including large language models (LLMs), achieve superior performance on multi-hop question-answering tasks. |
| Approach: | They propose to use the chain-of-thought mechanism to generate both the reasoning chain and the answer. |
| Outcome: | Empirical results show that the proposed framework generates more faithful reasoning chains and significantly improves the QA performance on two benchmark datasets. |
Chain-of-Rewrite: Aligning Question and Documents for Open-Domain Question Answering (2024.findings-emnlp)
Copied to clipboard
Chunlei Xin, Yaojie Lu, Hongyu Lin, Shuheng Zhou, Huijia Zhu, Weiqiang Wang, Zhongyi Liu, Xianpei Han, Le Sun
| Challenge: | Existing approaches to answer open-domain question have encountered term mismatch and limited interaction between IR systems and large language models. |
| Approach: | They propose a method which leverages the guidance and feedback gained from the analysis to provide faithful and consistent extensions for effective question answering. |
| Outcome: | Experiments on four open-domain question answering datasets show the proposed method performs well under zero-shot settings. |
From Query to Logic: Ontology-Driven Multi-Hop Reasoning in LLMs (2026.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) exhibit limitations in complex multi-hop question answering tasks that necessitate non-linear, structured reasoning. |
| Approach: | They propose an ontology-driven reasoning and chain framework that combines LLMs’ generative capabilities with the structural benefits of knowledge graphs. |
| Outcome: | Extensive experiments across a diverse set of models and standard MQA benchmarks demonstrate that the proposed framework achieves competitive performance while producing more interpretable reasoning chains. |
Chain-of-Questions Training with Latent Answers for Robust Multistep Question Answering (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for multistep question answering have shown promise in generating multistep solutions, but they lack robustness. |
| Approach: | They propose a framework that trains a model to robustly answer multistep questions by generating and answering sub-questions. |
| Outcome: | The proposed framework outperforms neuro-symbolic methods on a DROP contrast set and GPT-3.5 on QA adversarial sets. |
Towards Multi-Document Question Answering in Scientific Literature: Pipeline, Dataset, and Evaluation (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing QA systems do not strictly enforce cross-document synthesis or exploit the explicit inter-paper structure that links sources. |
| Approach: | They propose a pipeline methodology for constructing a multi-document academic QA dataset . they detect communities based on citation networks and leverage Large Language Models . |
| Outcome: | The proposed method generates QA pairs related to multi-document content automatically and forms coherent communities based on citation networks and large language models. |
Knowledge Extraction on Semi-Structured Content: Does It Remain Relevant for Question Answering in the Era of LLMs? (2026.eacl-long)
Copied to clipboard
Kai Sun, Yin Huang, Srishti Mehra, Mohammad Kachuee, Xilun Chen, Renjie Tao, Zhaojiang Lin, Andrea Jessee, Nirav Shah, Alex L Betty, Yue Liu, Anuj Kumar, Wen-tau Yih, Xin Luna Dong
| Challenge: | Existing literature on knowledge extraction for question answering questions whether it is still relevant for question answerrs. |
| Approach: | They extend an existing benchmark with knowledge extraction annotations and evaluate commercial and open-source LLMs of varying sizes. |
| Outcome: | The proposed model can achieve high QA accuracy, but can still benefit from knowledge extraction through augmentation with extracted triples and multi-task learning. |
Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs (2025.coling-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have shown impressive reasoning abilities when prompted with Chain-of-Thought (CoT). |
| Approach: | They propose to categorize Chain-of-X methods by taxonomies of nodes, i.e., the X in CoX, and application tasks, and then categorise them by taxanomies and discuss potential future directions. |
| Outcome: | The proposed methods are categorised by taxonomies of nodes, i.e., the X in CoX, and application tasks. |
Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key? (2024.acl-long)
Copied to clipboard
| Challenge: | Recent progress in LLMs discussion suggests that multi-agent discussion improves the reasoning abilities of LLM. |
| Approach: | They propose a group discussion framework to enrich the set of discussion mechanisms. |
| Outcome: | The proposed framework performs better on a wide range of reasoning tasks and backbone LLMs. |