Improving Retrieval Augmented Open-Domain Question-Answering with Vectorized Contexts (2024.findings-acl)
Copied to clipboard
| Challenge: | Retrieval Augmented Generation can be used to process long contexts in Open-Domain Question-Answering tasks. |
| Approach: | They propose a method to cover longer contexts in Open-Domain Question-Answering tasks by using a small encoder language model and cross-attention with origin inputs. |
| Outcome: | The proposed method can cover longer contexts while keeping the computing requirements close to the baseline. |
Similar Papers
Generation-Augmented Retrieval for Open-Domain Question Answering (2021.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to answer open-domain questions use sparse representations and sparsity. |
| Approach: | They propose a method which augments a query by generating relevant contexts from heuristically discovered contexts without external supervision. |
| Outcome: | The proposed approach outperforms state-of-the-art dense retrieval methods on natural questions and triviaQA datasets. |
Tagging-Augmented Generation: Assisting Language Models in Finding Intricate Knowledge In Long Contexts (2025.emnlp-industry)
Copied to clipboard
Anwesan Pal, Karen Hovsepian, Tinghao Guo, Mengnan Zhao, Somendra Tripathi, Nikos Kanakaris, George Mihaila, Sumit Nigam
| Challenge: | Recent studies into effective context lengths of flagship large language models (LLMs) have revealed major limitations in effective question answering (QA) and reasoning over long and complex contexts for even the largest and most impressive cadre of models. |
| Approach: | They propose a lightweight data augmentation strategy that boosts LLM performance in long-context scenarios without degrading and altering the integrity and composition of retrieved documents. |
| Outcome: | The proposed strategy boosts performance in long-context scenarios without degrading and altering the integrity and composition of retrieved documents. |
Dense Passage Retrieval for Open-Domain Question Answering (2020.emnlp-main)
Copied to clipboard
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih
| Challenge: | Open-domain question answering relies on efficient passage retrieval to select candidate contexts. |
| Approach: | They propose a dual-encoder framework that can be implemented to retrieve passages from a small number of questions and passages. |
| Outcome: | The proposed system outperforms a strong Lucene-BM25 system in top-20 passage retrieval accuracy on multiple open-domain QA benchmarks. |
Adaptive Contrastive Decoding in Retrieval-Augmented Generation for Handling Noisy Contexts (2024.findings-emnlp)
Copied to clipboard
Youna Kim, Hyuhng Joon Kim, Cheonbok Park, Choonghyun Park, Hyunsoo Cho, Junyeob Kim, Kang Min Yoo, Sang-goo Lee, Taeuk Kim
| Challenge: | Recent research has been developed to amplify contextual knowledge over parametric knowledge of large language models (LLMs) in knowledge-intensive tasks such as open-domain question-answering . |
| Approach: | They propose to amplify contextual knowledge over parametric knowledge of large language models (LLMs) by contrastive decoding to leverage contextual influence effectively. |
| Outcome: | The proposed approach improves open-domain question answering tasks especially in robustness by remaining undistracted by noisy contexts in retrieval-augmented generation. |
Awakening Augmented Generation: Learning to Awaken Internal Knowledge of Large Language Models for Question Answering (2025.coling-main)
Copied to clipboard
| Challenge: | Recent studies indicate that Large Language Models model rich knowledge, but it is often not activated and awakened. |
| Approach: | They propose a framework that leverages richer context to enhance question answering . Explicit awakening fine-tunes a context generator to create a synthetic, compressed document that functions as symbolic context. |
| Outcome: | The proposed framework mimics the human ability to answer questions using only thinking and recalling to compensate for knowledge gaps. |
A Copy-Augmented Generative Model for Open-Domain Question Answering (2022.acl-short)
Copied to clipboard
| Challenge: | Existing open-domain question answering approaches follow a two-stage paradigm retriever then reader. |
| Approach: | They propose a novel reader-based generative approach that incorporates extractive and generative readers. |
| Outcome: | The proposed model improves on two benchmark datasets, Natural Questions and TriviaQA. |
Context Quality Matters in Training Fusion-in-Decoder for Extractive Open-Domain Question Answering (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies have shown that the quantity and quality of context affect retrieval-augmented generation models during training. |
| Approach: | They propose a method to mitigate overfitting to specific context quality by introducing bias to the cross-attention distribution. |
| Outcome: | The proposed method improves retrieval-augmented generation models on different context quality. |
Beyond Prompting: An Efficient Embedding Framework for Open-Domain Question Answering (2025.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) have recently pushed open-domain question answering (ODQA) to new heights. |
| Approach: | They propose an embedding-level framework that enhances both the retriever and the reader by reordering query representations via lightweight linear layers under an unsupervised contrastive learning objective. |
| Outcome: | The proposed framework outperforms baselines in accuracy and efficiency across three open-source LLMs, three retrieval methods, and four ODQA benchmarks. |
xMoCo: Cross Momentum Contrastive Learning for Open-Domain Question Answering (2021.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to find relevant passages using sparse keywords are not effective for open domain question answering. |
| Approach: | They propose a new contrastive learning method for learning a dual-encoder model for question-passage matching using a large pool of negative samples. |
| Outcome: | The proposed method maintains large pool of negative samples and optimizes question-to-passage and passage-to question matching tasks. |
Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Recent work has proposed to improve relevance modeling by having large language models actively involved in retrieval, i.e., to guide retrieval with generation. |
| Approach: | They propose to have large language models actively involved in retrieval to guide retrieval with generation. |
| Outcome: | The proposed method synergizes retrieval and generation in an iterative manner, and can generate better results in subsequent iterations. |