Knowledge-Augmented Question Error Correction for Chinese Question Answer System with QuestionRAG (2025.emnlp-industry)
Copied to clipboard
| Challenge: | Large language models struggle with input errors, often failing to interpret user intent or altering the original question’s structure (over-correction). |
| Approach: | They propose a framework that uses reinforcement learning to address misinterpretation and over-correction by integrating external knowledge with the input. |
| Outcome: | The proposed framework unlocks the full potential of LLMs for the question correction task. |
Similar Papers
SimRAG: Self-Improving Retrieval-Augmented Generation for Adapting Large Language Models to Specialized Domains (2025.naacl-long)
Copied to clipboard
Ran Xu, Hui Liu, Sreyashi Nag, Zhenwei Dai, Yaochen Xie, Xianfeng Tang, Chen Luo, Yang Li, Joyce C. Ho, Carl Yang, Qi He
| Challenge: | Retrieval-augmented generation (RAG) enhances the question answering abilities of large language models (LLMs) however, adapting general-purpose RAG systems to specialized fields poses unique challenges due to distribution shifts and limited access to domain-specific data. |
| Approach: | They propose a method that equips large language models with joint capabilities of question answering and question generation for domain adaptation. |
| Outcome: | Experiments on 11 datasets across three different domains verify the efficacy of SimRAG over baselines by 1.2%–8.6%. |
BIDER: Bridging Knowledge Inconsistency for Efficient Retrieval-Augmented LLMs via Key Supporting Evidence (2024.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) have demonstrated efficacy in knowledge-intensive tasks such as open-domain QA, but inconsistencies between retrieval knowledge and the necessary knowledge for LLMs, leading to a decline in LLM’s answer quality. |
| Approach: | They propose a retrieval-augmented large language model that refines retrieval documents into Key Supporting Evidence (KSE) through knowledge synthesis, supervised fine-tuning, and preference alignment. |
| Outcome: | The proposed model outperforms existing methods in five datasets and boosts answer quality by 7% while reducing input content length by 80%. |
Search if you don’t know! Knowledge-Augmented Korean Grammatical Error Correction with Large Language Models (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies have shown that the performance of large language models is insufficient for non-English data, such as Korean. |
| Approach: | They propose a framework that integrates evidential information from external sources into the prompt for the Korean GEC task. |
| Outcome: | The proposed framework extracts salient phrases from the given source and retrieves non-parametric knowledge based on these phrases. |
Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity (2024.naacl-long)
Copied to clipboard
| Challenge: | Recent Large Language Models (LLMs) generate factually incorrect answers based on their parametric memory. |
| Approach: | They propose a retrieval-augmented large language model that can dynamically select the most suitable strategy based on query complexity. |
| Outcome: | The proposed approach improves the performance of QA systems on open-domain QA datasets. |
From Problem-Solving to Teaching Problem-Solving: Aligning LLMs with Pedagogy using Reinforcement Learning (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) are often optimized for direct question-answering, but their effectiveness is often undermined by strategically withholding answers. |
| Approach: | They propose an online reinforcement learning-based alignment framework that can quickly adapt LLMs into effective tutors using simulated student-tutor interactions. |
| Outcome: | The proposed model outperforms proprietary models like LearnLM and can be used to enhance interpretability and pedagogical quality. |
Refiner: Restructure Retrieved Content Efficiently to Advance Question-Answering Capabilities (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are limited by their parametric knowledge, leading to hallucinations in knowledge-extensive tasks. |
| Approach: | They propose an end-to-end extract-and-restructure paradigm that leverages a single decoder-only LLM to adaptively extract query-relevant contents verbatim along with the necessary context. |
| Outcome: | Experiments show that a trained Refiner outperforms state-of-the-art RAG and compressing approaches in multiple tasks. |
KERAG: Knowledge-Enhanced Retrieval-Augmented Generation for Advanced Question Answering (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Traditional Knowledge Graph Question Answering (KGQA) methods rely on semantic parsing to retrieve knowledge strictly necessary for answer generation. |
| Approach: | They propose a retrieval-filtering-summarization pipeline that enhances QA coverage by retrieving a broader subgraph likely to contain relevant information. |
| Outcome: | The proposed pipeline surpasses state-of-the-art solutions by about 7% in quality and exceeds GPT-4o (Tool) by 10-21%. |
MuRAG: Multimodal Retrieval-Augmented Generator for Open Question Answering over Images and Text (2022.emnlp-main)
Copied to clipboard
| Challenge: | Pre-trained language models store a massive amount of world knowledge implicitly in their parameters, but large models often fail to encode information about rare entities and events. |
| Approach: | They propose a retrieval-augmented model which accesses an external non-parametric memory to augment language generation. |
| Outcome: | The proposed model outperforms existing models by 10-20% absolute on two datasets and under distractor and full-wiki settings. |
Align Documents to Questions: Question-Oriented Document Rewriting for Retrieval-Augmented Generation (2026.findings-acl)
Copied to clipboard
| Challenge: | Retrieval-Augmented Generation (RAG) enhances the factuality of Large Language Models (LLMs) however, LLMs exhibit a stylistic bias when presented with mixed contexts, revealing a bottleneck in their utility. |
| Approach: | They propose a style-controlled rewriter that aligns retrieved documents with a question-oriented style while preserving facts. |
| Outcome: | The proposed model improves RAG pipelines by 8% with negligible latency overhead. |
Knowledge Corpus Error in Question Answering (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Recent work in open-domain question answering (QA) has explored generating context passages from large language models (LLMs) however, it is not well understood why generated passages can be more effective than retrieved ones. |
| Approach: | They propose to generate context passages from large language models by paraphrasing human-annotated gold context using LLMs to observe knowledge corpus error. |
| Outcome: | The proposed framework shows that paraphrasing human-annotated gold contexts improves performance over retrieval steps. |