Browse State-of-the-Art › Retrieval-augmented Few-shot In-context Audio Captioning
Retrieval-augmented Few-shot In-context Audio Captioning
5 papers with code · 1 benchmark · 1 dataset archive 2025-07-28
Retrieval-augmented few-shot in-context audio captioning is a specialized approach within the broader domain of audio captioning. This technique leverages the principles of few-shot in-context learning, akin to those used in LLMs, to generate textual descriptions for audio content without training on the dataset. Instead, during inference, the model utilizes a few-shot retrieval method where a few selected examples from the training data are presented in-context. This allows the model to generate accurate and contextually relevant captions based on limited input.
Description from the archive archive 2025-07-28.
Benchmarks archive 2025-07-28
1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.
| Dataset | Best model (first row in archive order) | Paper | Code | Syntology | Compare |
|---|---|---|---|---|---|
| AudioCaps (5 rows) | Audio Flamingo (4-shot) | Audio Flamingo: A Novel Audio Language Model with Few-Shot... | code | Syntology ran 3 of 3 samples · 0 unverified | Compare |
Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.
Libraries
Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.
Datasets archive 2025-07-28
1 dataset whose archive record lists this task, ordered by the archive's paper count.
Subtasks archive 2025-07-28
No subtask under this task in the archive's task tree.
Parent tasks archive 2025-07-28
Most implemented papers archive 2025-07-28
5 shown of 5 papers with code (5 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
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2 Feb 2024 1 repository listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)Augmenting large language models (LLMs) to understand audio -- including non-speech sounds and non-verbal speech -- is critically important for diverse real-world applications of LLMs.
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18 Sep 2023 1 repository listed Syntology ran 5 of 8 samples · 3 unverified · 8 pointer-only (licence)We present RECAP (REtrieval-Augmented Audio CAPtioning), a novel and effective audio captioning system that generates captions conditioned on an input audio and other captions similar to the audio retrieved from a…
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30 Mar 2023 1 repository listedAudio captioning aims to generate text descriptions from environmental sounds.
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15 Nov 2021 1 repository listedutomated audio captioning is the multimodal task of describing environmental audio recordings with fluent natural language.
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21 Jul 2021 1 repository listedIn this paper, we propose an Audio Captioning Transformer (ACT), which is a full Transformer network based on an encoder-decoder architecture and is totally convolution-free.
Syntology lines on 2 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.
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