OPEN-DOMAIN QA

Open-Domain Question Answering (2020.acl-tutorials)

Challenge:	Existing methods for retrieving knowledge from a single source homogeneous corpus have been gaining increasing attention in the field of natural language processing (NLP) however, they still suffer from the following drawbacks: (i) They are usually trained offline, making the model agnostic to the latest information, e.g., asking a chat-bot about COVID-19.
Approach:	They propose to use a single-source homogeneous corpus to generate retrieval-augmented generation models that can learn from the pre-training corpus.
Outcome:	The proposed methods have been applied to various knowledge-intensive NLP tasks, but most of the work has focused on retrieving unstructured text documents from Wikipedia.

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Danqi Chen, Wen-tau Yih

Grape: Knowledge Graph Enhanced Passage Reader for Open-domain Question Answering (2022.findings-emnlp)

Challenge:	tutorial provides a comprehensive overview of cutting-edge research in open-domain question answering (QA)
Approach:	tutorial provides a comprehensive overview of cutting-edge research in open-domain question answering . focus will shift to cutting- edge models proposed for open- domain QA .
Outcome:	The tutorial will cover cutting-edge research in open-domain question answering (QA) it will cover two-stage retriever-reader approaches, dense retriever and end-to-end training, and retriever free methods .

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Mingxuan Ju, Wenhao Yu, Tong Zhao, Chuxu Zhang, Yanfang Ye

Structured Data in NLP

Challenge:	Open-domain question answering (QA) models employ a retriever-reader pipeline . however, state-of-the-art readers fail to capture complex relationships between entities .
Approach:	They propose a knowledge graph enhanced passage reader that captures entities in questions and retrieved passages.
Outcome:	The proposed knowledge graph enhanced passage reader improves on open-domain QA benchmarks by up to 2.2 exact match scores.

MinPrompt: Graph-based Minimal Prompt Data Augmentation for Few-shot Question Answering (2024.acl-long)

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Xiusi Chen, Jyun-Yu Jiang, Wei-Cheng Chang, Cho-Jui Hsieh, Hsiang-Fu Yu, Wei Wang

Training a Ranking Function for Open-Domain Question Answering (N18-4)

Challenge:	Recent advances in few-shot question answering rely on pre-trained large language models and fine-tuning in specific settings.
Approach:	They propose to select the most informative data for fine-tuning to improve efficiency . they use an approximate graph algorithm and unsupervised question generation to generate QA pairs .
Outcome:	The proposed framework improves the performance of the few-shot question answering task on the open-domain QA task.

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Phu Mon Htut, Samuel Bowman, Kyunghyun Cho

Question Answering

Challenge:	Recent advances in machine reading have inspired researchers to combine Information Retrieval with machine reading to tackle open-domain QA.
Approach:	They propose two neural network rankers that assign scores to different passages based on their likelihood of containing the answer to a given question.
Outcome:	The proposed models achieve human level performance in open-domain QA compared to reading comprehension-style QA because it is difficult to retrieve the pieces of paragraphs that contain the answer to the question.

BIDER: Bridging Knowledge Inconsistency for Efficient Retrieval-Augmented LLMs via Key Supporting Evidence (2024.findings-acl)

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Jiajie Jin, Yutao Zhu, Yujia Zhou, Zhicheng Dou

Green & Sustainable NLP

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%.

Open Domain Question Answering over Tables via Dense Retrieval (2021.naacl-main)

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Jonathan Herzig, Thomas Müller, Syrine Krichene, Julian Eisenschlos

Open-Domain Question Answering Goes Conversational via Question Rewriting (2021.naacl-main)

Challenge:	Recent advances in open-domain QA focus on retrieving textual passages . a retriever designed to handle tabular context can improve retrieval quality .
Approach:	They propose a tabular-based retrieval model that improves retrieval quality over a BERT-based retriever.
Outcome:	The proposed retriever improves retrieval quality with mined hard negatives over a BERT-based retriever.

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Raviteja Anantha, Svitlana Vakulenko, Zhucheng Tu, Shayne Longpre, Stephen Pulman, Srinivas Chappidi

RaFe: Ranking Feedback Improves Query Rewriting for RAG (2024.findings-emnlp)

Challenge:	Existing large-scale benchmarks for conversational QA limit the topic of conversation to the content of a single document.
Approach:	They propose a dataset for Question Rewriting in Conversational Context (QReCC) the dataset contains 14K conversations with 80K question-answer pairs.
Outcome:	The proposed approach shows that the first baseline for the QReCC dataset is 19.10, compared to the human upper bound of 75.45, indicating the difficulty of the setup and a large room for improvement.

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Shengyu Mao, Yong Jiang, Boli Chen, Xiao Li, Peng Wang, Xinyu Wang, Pengjun Xie, Fei Huang, Huajun Chen, Ningyu Zhang

Text Generation

Challenge:	Large Language Models (LLMs) and Retrieval Augmentation Generation (RAG) techniques have evolved to enhance document retrieval by reformulating queries.
Approach:	They propose a framework for training query rewriting models that leverages a reranker framework.
Outcome:	The proposed framework provides ranking feedback aligned well with the rewriting objectives without needing signals from annotations and supports both online and offline training models.

Ranking Paragraphs for Improving Answer Recall in Open-Domain Question Answering (D18-1)

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Jinhyuk Lee, Seongjun Yun, Hyunjae Kim, Miyoung Ko, Jaewoo Kang

Question Answering

Challenge:	Recent work has combined open-domain question answering with machine comprehension models to find answers in a large knowledge source.
Approach:	They propose a machine comprehension model that ranks paragraphs of retrieved documents for a higher answer recall with less noise.
Outcome:	The proposed model improves on four open-domain QA datasets by 7.8% on average.

RAG4ITOps: A Supervised Fine-Tunable and Comprehensive RAG Framework for IT Operations and Maintenance (2024.emnlp-industry)

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Tianyang Zhang, Zhuoxuan Jiang, Shengguang Bai, Tianrui Zhang, Lin Lin, Yang Liu, Jiawei Ren

Two-Step Question Retrieval for Open-Domain QA (2022.findings-acl)

Challenge:	Large Language Models (LLMs) have improved the open-domain QA’s performance, but how to efficiently handle enterprise-exclusive corpora and build domain-specific QA systems are still not studied for industrial applications.
Approach:	They propose a general and comprehensive framework based on Retrieval Augmented Generation (RAG) and facilitate the whole business process of establishing QA systems for IT operations and maintenance.
Outcome:	The proposed framework achieves superior results on two kinds of QA tasks.

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Yeon Seonwoo, Juhee Son, Jiho Jin, Sang-Woo Lee, Ji-Hoon Kim, Jung-Woo Ha, Alice Oh

Learning to Paraphrase for Alignment with LLM Preference (2024.findings-emnlp)

Challenge:	Existing question retrieval models have shown a significant increase in inference speed but at the cost of lower QA performance compared to the retriever-reader pipeline.
Approach:	They propose a two-step question retrieval model with distant supervision to improve inference speed.
Outcome:	The proposed model significantly increases the performance of existing question retrieval models with a negligible loss on inference speed.

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Junbo Fu, Guoshuai Zhao, Yimin Deng, Yunqi Mi, Xueming Qian

Text Generation

Challenge:	Large Language Models (LLMs) exhibit the issue of paraphrase divergence, which means that when a question is phrased in a slightly different but semantically similar way, LLM may output a wrong response . retraining faces challenges in meeting the computational costs and privacy security demands of LLMs.
Approach:	They propose a black-box method that enhances model performance by paraphrasing questions in expressions preferred by the model.
Outcome:	The proposed method improves performance by paraphrasing questions in expressions preferred by the model.

Why So Gullible? Enhancing the Robustness of Retrieval-Augmented Models against Counterfactual Noise (2024.findings-naacl)

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Giwon Hong, Jeonghwan Kim, Junmo Kang, Sung-Hyon Myaeng, Joyce Whang

BERTAC: Enhancing Transformer-based Language Models with Adversarially Pretrained Convolutional Neural Networks (2021.acl-long)

Challenge:	Existing retrieval-augmented language models assume query relevance and irrelevance as dichotomy . existing models are highly brittle to the presence of conflicting information in both the fine-tuning and in-context few-shot learning scenarios.
Approach:	They propose methods for handling knowledge conflicts by fine-tuning a discriminator or prompting it to elicit its discriminative capability.
Outcome:	The proposed approaches significantly enhance model robustness on open-domain QA.

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Jong-Hoon Oh, Ryu Iida, Julien Kloetzer, Kentaro Torisawa

Semantic Text Processing

Challenge:	Existing models of NLP are fading away, but new ones are needed to maintain their dominance.
Approach:	They propose a method to pretrain a CNN using Wikipedia data and integrate it with standard TLMs.
Outcome:	The proposed method outperforms the original ALBERT on GLUE tasks and achieves similar performance to SOTA on open-domain QA tasks.

Collaborative Chain-of-Agents for Parametric-Retrieved Knowledge Synergy (2026.acl-long)

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Yi Jiang, Sendong Zhao, Jianbo Li, Haochun Wang, Lizhe Zhang, Yan Liu, Bing Qin

UnitedQA: A Hybrid Approach for Open Domain Question Answering (2021.acl-long)

Challenge:	Existing RAG methods focus on external retrieval, while ignoring the rich content of the model.
Approach:	They propose a framework that enhances explicit synergy over parametric and retrieved knowledge by integrating external retrieval components into the input context of the LLMs.
Outcome:	The proposed framework enhances explicit synergy over parametric and retrieved knowledge.

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Hao Cheng, Yelong Shen, Xiaodong Liu, Pengcheng He, Weizhu Chen, Jianfeng Gao

DeAR: Dual-Stage Document Reranking with Reasoning Agents via LLM Distillation (2025.findings-emnlp)

Challenge:	Recent work on open-domain question answering focuses on either extractive or generative readers exclusively.
Approach:	They propose a hybrid approach to extractive and generative readers that leverages both models.
Outcome:	The proposed approach outperforms state-of-the-art models on NaturalQuestions and TriviaQA respectively.

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Abdelrahman Abdallah, Jamshid Mozafari, Bhawna Piryani, Adam Jatowt

Evaluating Open-Domain Question Answering in the Era of Large Language Models (2023.acl-long)

Challenge:	DeAR is an open-source framework that decouples the tasks of LLMs with holistic cross-document analysis.
Approach:	They propose an open-source framework that decouples relevance scoring with holistic cross-document analysis.
Outcome:	The proposed framework outperforms open-source frameworks in QA and open-domain QA.

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Ehsan Kamalloo, Nouha Dziri, Charles Clarke, Davood Rafiei

Retrieval Augmentation Reduces Hallucination in Conversation (2021.findings-emnlp)

Challenge:	Existing evaluation models fail to identify lexical matching failures for open-domain question answering.
Approach:	They manually evaluate open-domain QA models by manually evaluating their answers on a popular benchmark.
Outcome:	The proposed model performs better on NQ-open than existing models and more than 50% of lexical matching failures are attributed to semantically equivalent answers.

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Kurt Shuster, Spencer Poff, Moya Chen, Douwe Kiela, Jason Weston

Query Rewriting in Retrieval-Augmented Large Language Models (2023.emnlp-main)

Challenge:	State-of-the-art dialogue models suffer from factual incorrectness and hallucination of knowledge.
Approach:	They propose to use neural-retrieval-in-the-loop architectures to optimize knowledge-grounded dialogue by retrieving, ranking, and encoder-decoders.
Outcome:	The proposed architectures exhibit open-domain conversational capabilities and generalize effectively to scenarios not within the training data.

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Xinbei Ma, Yeyun Gong, Pengcheng He, Hai Zhao, Nan Duan

Semantic Text Processing

Challenge:	Existing studies focus on adapting either the retriever or the reader, but this approach is more focused on adaptation of the query itself.
Approach:	They propose a new framework for retrieval-augmented Large Language Models . they propose rewrite-retrieve-read instead of retrieve-then-read .
Outcome:	The proposed framework improves performance on downstream tasks, open-domain QA and multiple-choice QA.

FiD-ICL: A Fusion-in-Decoder Approach for Efficient In-Context Learning (2023.acl-long)

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Qinyuan Ye, Iz Beltagy, Matthew Peters, Xiang Ren, Hannaneh Hajishirzi

Shall We Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Study (2023.emnlp-main)

Challenge:	Large pre-trained models are capable of few-shot in-context learning (ICL) however, concatenated demonstrations are often excessively long and require additional computation.
Approach:	They propose to apply fusion-in-decoder (FiD) models to perform few-shot in-context learning (ICL) they propose to use concatenation-based, early-fusion, intermediate- and late-fusion methods to improve efficiency .
Outcome:	The proposed methods outperform concatenation-based models on 11 held-out tasks.

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Boxin Wang, Wei Ping, Peng Xu, Lawrence McAfee, Zihan Liu, Mohammad Shoeybi, Yi Dong, Oleksii Kuchaiev, Bo Li, Chaowei Xiao, Anima Anandkumar, Bryan Catanzaro

AGRaME: Any-Granularity Ranking with Multi-Vector Embeddings (2024.emnlp-main)

Challenge:	a recent study shows that retrieval-augmented LMs can improve text generation quality and accuracy.
Approach:	They propose a model that reproduces RETRO parameters while retrieving a text corpus . they find RETRO outperforms GPT on text generation with less repetition .
Outcome:	The proposed model outperforms standard retrieval-augmented GPT and retrieval augmented GTP on text generation and accuracy tasks.

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Revanth Gangi Reddy, Omar Attia, Yunyao Li, Heng Ji, Saloni Potdar

Representation Learning

Challenge:	Existing ranking algorithms restrict granularity to full passages or require a specific dense index for each desired level of granules.
Approach:	They propose a multi-vector ranking approach that leverages multi-vctor embeddings to rank at varying levels of granularity while maintaining encoding at a single (coarser) level of grail.
Outcome:	The proposed method surpasses prompt-driven citation generation by incorporating proposition-level ranking to post-hoc citation addition.

Multi-stage Training with Improved Negative Contrast for Neural Passage Retrieval (2021.emnlp-main)

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Jing Lu, Gustavo Hernandez Abrego, Ji Ma, Jianmo Ni, Yinfei Yang

Learning Dense Representations of Phrases at Scale (2021.acl-long)

Challenge:	Existing neural firststage retrieval models overcome lexical gap issue by projecting query and document to a shared dense space.
Approach:	They propose a multi-stage framework for neural passage retrieval using synthetic data, negative sampling, and fusion techniques.
Outcome:	The proposed framework improves retrieval accuracy and enhances the negative contrast in both stages.

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Jinhyuk Lee, Mujeen Sung, Jaewoo Kang, Danqi Chen

QPaug: Question and Passage Augmentation for Open-Domain Question Answering of LLMs (2024.findings-emnlp)

Challenge:	Existing phrase retrieval models rely on sparse representations and still underperform retriever-reader approaches.
Approach:	They propose a method to learn phrase representations from reading comprehension tasks using negative sampling methods.
Outcome:	The proposed model improves over previous models by 15%-25% absolute accuracy and matches the performance of state-of-the-art retrieval models.

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Minsang Kim, Cheoneum Park, Seung Baek

UR2 : Unify RAG and Reasoning through Reinforcement Learning (2026.acl-long)

Challenge:	Existing approaches to augmented generation of retrieved passages rely on the quality of a question's retrieved information.
Approach:	They propose a simple yet efficient method called question and passage augmentation via LLMs for open-domain QA.
Outcome:	The proposed method outperforms the state-of-the-art and achieves significant performance gain over existing methods.

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Weitao Li, Boran Xiang, Xiaolong Wang, Jingyi Ren, Ante Wang, Zhinan Gou, Weizhi Ma, Yang Liu

MLEC-QA: A Chinese Multi-Choice Biomedical Question Answering Dataset (2021.emnlp-main)

Challenge:	Existing attempts to unify large language models are limited to open-domain QA with fixed retrieval settings.
Approach:	They propose a general reinforcement learning framework that dynamically coordinates retrieval and reasoning.
Outcome:	The proposed framework outperforms existing paradigms on open-domain QA, MMLU-Pro, medical, and mathematical reasoning tasks.

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Jing Li, Shangping Zhong, Kaizhi Chen

LORAXBENCH: A Multitask, Multilingual Benchmark Suite for 20 Indonesian Languages (2025.emnlp-main)

Challenge:	Question Answering (QA) is a branch of QA that enables effective perceiving, accessing, and understanding complex biomedical knowledge by innovative applications.
Approach:	They present MLEC-QA, the largest-scale Chinese multi-choice biomedical QA dataset . they implement eight representative control methods and open-domain QA methods as baselines .
Outcome:	The proposed dataset is the largest-scale Chinese multi-choice biomedical QA dataset . it covers the following biomedically-relevant sub-fields: Clinic, Stomatology, Public Health, Traditional Chinese Medicine, and Traditional Chinese medicine Combined with Western Medicine.

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Alham Fikri Aji, Trevor Cohn

Dense Retrieval as Indirect Supervision for Large-space Decision Making (2023.findings-emnlp)

Challenge:	LORAXBENCH is a benchmark for low-resource languages of Indonesia . it covers reading comprehension, open domain QA, language inference, causal reasoning, translation, and cultural question answering across 20 languages.
Approach:	They propose a benchmark that focuses on low-resource languages of Indonesia and covers 6 diverse tasks: reading comprehension, open-domain QA, language inference, causal reasoning, translation, and cultural question answering.
Outcome:	The proposed benchmark covers reading comprehension, open-domain QA, language inference, causal reasoning, translation, and cultural question answering across 20 Indonesian languages.

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Nan Xu, Fei Wang, Mingtao Dong, Muhao Chen

Efficient Context Selection for Long-Context QA: No Tuning, No Iteration, Just Adaptive‐k (2025.emnlp-main)

Challenge:	Dense Decision Retrieval (DDR) is a learning-to-retrieve task for discriminative natural language understanding (NLU) tasks with large label spaces.
Approach:	They propose a novel approach to learning large-space discriminative NLU tasks as a learning-to-retrieve task by adopting a dual-encoder architecture that learns to predict by retrieving from a decision thesaurus.
Outcome:	The proposed approach outperforms baselines greatly on multi-label classification tasks, 1.17% in F1 score ultra-fine entity typing, and 1.26% in accuracy on three few-shot intent classification tasks on average.

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Chihiro Taguchi, Seiji Maekawa, Nikita Bhutani

AdaFuse: Adaptive Ensemble Decoding for Large Language Models (2026.acl-long)

Challenge:	Existing adaptive methods struggle with aggregation QA where optimal external context is unknown and variable.
Approach:	They propose a single-pass method that selects a query-specific number of passages . Adaptivek retrieval matches or outperforms fixedk baselines while using 10x fewer tokens compared to full-context input .
Outcome:	Adaptivek retrieval matches or outperforms fixedk baselines on factoid and aggregation QA benchmarks . it uses 10x fewer tokens than full-context input and still retrieves 70% of relevant passages compared to previous methods .

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Chengming Cui, Tianxin Wei, Ziyi Chen, Ruizhong Qiu, Zhichen Zeng, Zhining Liu, Xuying Ning, Duo Zhou, Jingrui He