REVIEW 7 cited by
Generate rather than Retrieve: Large Language Models are Strong Context Generators
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
Knowledge-intensive tasks, such as open-domain question answering (QA), require access to a large amount of world or domain knowledge. A common approach for knowledge-intensive tasks is to employ a retrieve-then-read pipeline that first retrieves a handful of relevant contextual documents from an external corpus such as Wikipedia and then predicts an answer conditioned on the retrieved documents. In this paper, we present a novel perspective for solving knowledge-intensive tasks by replacing document retrievers with large language model generators. We call our method generate-then-read (GenRead), which first prompts a large language model to generate contextutal documents based on a given question, and then reads the generated documents to produce the final answer. Furthermore, we propose a novel clustering-based prompting method that selects distinct prompts, resulting in the generated documents that cover different perspectives, leading to better recall over acceptable answers. We conduct extensive experiments on three different knowledge-intensive tasks, including open-domain QA, fact checking, and dialogue system. Notably, GenRead achieves 71.6 and 54.4 exact match scores on TriviaQA and WebQ, significantly outperforming the state-of-the-art retrieve-then-read pipeline DPR-FiD by +4.0 and +3.9, without retrieving any documents from any external knowledge source. Lastly, we demonstrate the model performance can be further improved by combining retrieval and generation. Our code and generated documents can be found at https://github.com/wyu97/GenRead.
Forward citations
Cited by 7 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.
-
MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation
Contrasting trajectories from heterogeneous LLM agents yields shared abstract reasoning constraints that transfer better than single-model or naively transferred memory.
-
Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation
In a 430k-evaluation study, plain baseline prompting beats most elaborate prompting techniques on non-reasoning LLMs across MCQA benchmarks, with only small role-framing variants gaining about 3 percentage points.
-
Generative Recommendation with Semantic IDs: A Practitioner's Handbook
An open-source framework and ablation study showing which semantic-ID generative recommendation components actually matter, with results that challenge several standard defaults.
-
GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis
GainRAG aligns retriever and LLM preferences by training a selector on contrastive-perplexity 'gain' signals plus a pseudo-passage fallback, improving RAG accuracy on six QA datasets.
-
QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting
A contextual bandit that chooses among five query-rewrite strategies, conditioned on 17 linguistic features, reduces LLM hallucination on QA benchmarks and beats static prompting and no-rewrite baselines.
-
Thinking About Thinking: SAGE-nano's Inverse Reasoning for Self-Aware Language Models
A 4B-parameter model is claimed to explain its own reasoning through inverse attention analysis, but the paper offers no consistent evidence or artifacts.
Discussion (0). Continue with ORCID to comment.