---
title: "LiveLongBench — paper by Wu et al. (2026)"
description: "They define six tasks grouped into retrieval, reasoning and hybrid categories, with both open- and… 44 references, cited by 1 paper here."
authors: ["Yongxuan Wu", "Runyu Chen", "Peiyu Liu", "Hongjin Qian"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.1485/"
doi: "10.18653/v1/2026.findings-acl.1485"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wu-2026-livelongbench-tackling-long-context-understanding/"
---
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# LiveLongBench: Tackling Long-Context Understanding for Spoken Texts from Live Streams

**Authors:** Yongxuan Wu, [Runyu Chen](https://md.research.elamur.ai/free/en/authors/runyu-chen/), [Peiyu Liu](https://md.research.elamur.ai/free/en/authors/peiyu-liu/) and [Hongjin Qian](https://md.research.elamur.ai/free/en/authors/hongjin-qian/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.1485/) · [PDF](https://aclanthology.org/2026.findings-acl.1485.pdf) · [DOI 10.18653/v1/2026.findings-acl.1485](https://doi.org/10.18653/v1/2026.findings-acl.1485) · [OpenAlex](https://openalex.org/W4415307261)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · 1 in our library, of the 149,439 papers whose bibliographies we read

## Summary
Current long-context benchmarks are built mostly from polished written text. Real conversational speech is different: it is full of fillers, repetition, fragmented syntax and topic shifts. The authors address this gap with LiveLongBench, a benchmark built from transcripts of Douyin e-commerce live streams. Its documents average about 97K tokens and some exceed 500K. They define six tasks grouped into retrieval, reasoning and hybrid categories, with both open- and closed-ended questions. Evidence can sit in one span, several spans, or the whole document, and they add a new "semantic multi-span" type in which related evidence is spread conceptually across the transcript.

Testing LLMs and the context-compression methods KIVI, MInference and LLMLingua showed that each approach favours certain tasks and none wins everywhere. Redundant input causes clear performance drops. Combining compression methods often beats using any single one. A Data Envelopment Analysis (DEA) ranks these combinations by their trade-off between memory and accuracy, giving practical guidance for spoken-language and e-commerce applications.

## Key points
- **The data:** LiveLongBench is presented as the first long-context benchmark built from live-stream speech. Transcripts come from Douyin e-commerce sessions, have personal information removed, and are released for research use only. Code and data are at github.com/Yarayx/livelongbench.
- **Single-method result:** Stronger compression can act as a denoiser. In one comparison, LLMLingua at 4× scored higher than at 2× (36.7 vs. 33.3). The benefit was not uniform: reasoning gained more than retrieval.
- **Best accuracy:** MInference+LLMLingua-4× gave the best overall balance of retrieval and reasoning. MInference+LLMLingua-2× was strongest on reasoning-heavy tasks.
- **Best efficiency:** KIVI-4bit+MInference+LLMLingua-2× used the least memory while still outperforming KIVI or MInference alone. The DEA analysis, which treats memory as input and average score as output, rated this three-way hybrid the most efficient configuration.
- **Limitations:** The benchmark covers only the live-stream domain. Evaluation also depends on costly annotation by bilingual experts.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Beijing Academy of Artificial Intelligence](https://md.research.elamur.ai/free/en/labs/beijing-academy-of-artificial-intelligence/) — Research institute, Beijing, China · 32 works · 7,511 citations
- [University of International Business and Economics](https://md.research.elamur.ai/free/en/universities/university-of-international-business-and-economics/) — University, Beijing, China · 7 works · 37 citations

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## Cite

Cite as: Yongxuan Wu, Runyu Chen, Peiyu Liu and Hongjin Qian. 2026. LiveLongBench: Tackling Long-Context Understanding for Spoken Texts from Live Streams. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{wu2026livelongbench,
  title = {{LiveLongBench: Tackling Long-Context Understanding for Spoken Texts from Live Streams}},
  author = {Yongxuan Wu, Runyu Chen, Peiyu Liu and Hongjin Qian},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1485},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1485}
}
```

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*Updated 1 November 2026*