REVIEW 4 cited by
Generator-Retriever-Generator Approach for Open-Domain Question Answering
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Open-domain question answering (QA) tasks usually require the retrieval of relevant information from a large corpus to generate accurate answers. We propose a novel approach called Generator-Retriever-Generator (GRG) that combines document retrieval techniques with a large language model (LLM), by first prompting the model to generate contextual documents based on a given question. In parallel, a dual-encoder network retrieves documents that are relevant to the question from an external corpus. The generated and retrieved documents are then passed to the second LLM, which generates the final answer. By combining document retrieval and LLM generation, our approach addresses the challenges of open-domain QA, such as generating informative and contextually relevant answers. GRG outperforms the state-of-the-art generate-then-read and retrieve-then-read pipelines (GENREAD and RFiD) improving their performance by at least by +5.2, +4.2, and +1.6 on TriviaQA, NQ, and WebQ datasets, respectively. We provide code, datasets, and checkpoints at https://github.com/abdoelsayed2016/GRG.
Forward citations
Cited by 4 Pith papers
-
How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models
On a new benchmark of post-April 2025 queries, LLM rerankers show a 5-15% performance drop compared with familiar benchmarks, and lightweight models match them on efficiency and sometimes accuracy.
-
Iterative Self-Incentivization Empowers Large Language Models as Agentic Searchers
EXSEARCH trains LLMs for agentic search by treating search trajectories as latent variables and optimizing a weighted likelihood via expectation-maximization, yielding gains on NQ, HotpotQA, MuSiQue, and 2WikiQA.
-
Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques
A survey that maps LLM applications, vulnerabilities, and defenses across eight cybersecurity domains, but with significant citation and rigor problems.
-
An Evaluation of Large Language Models on Text Summarization Tasks Using Prompt Engineering Techniques
A broad benchmark of six open-weights LLMs shows prompt design and chunking affect summarization quality more than model size alone.
Discussion (0). Continue with ORCID to comment.