Evaluating Zero-Shot Long-Context LLM Compression

Chenyu Wang; Kai Li; Yihan Wang

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Evaluating Zero-Shot Long-Context LLM Compression

Chenyu Wang, Yihan Wang, Kai Li

Computation and Language · Submitted 10 Jun 2024

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We identify the tendency for computational errors to increase under long-context when employing certain compression methods.

how it is stated · asserted 0.93 read the record →

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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,737 input tokens.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.96 · human participants 0.03 · controlled comparison 0.13 · randomized assignment 0.02 · states quantitative result 0.07 · compares with prior results 0.24 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; show every role.

This study evaluates the effectiveness of zero-shot compression techniques on large language models (LLMs) under long-context. We identify the tendency for computational errors to increase under long-context when employing certain compression methods. We propose a hypothesis to explain the varied behavior of different LLM compression techniques and explore remedies to mitigate the performance decline observed in some techniques under long-context. This is a course report for COS 598D Machine Learning and Systems by Prof. Kai Li at Princeton University. Due to limited computational resources, our experiments were conducted only on LLaMA-2-7B-32K.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	This study evaluates the effectiveness of zero-shot compression techniques on large language models (LLMs) under long-context.	contribution 0.98	0.82	0.06	asserted 0.79
s1	We identify the tendency for computational errors to increase under long-context when employing certain compression methods.	result 1.00	0.85	0.25	asserted 0.93
s2	We propose a hypothesis to explain the varied behavior of different LLM compression techniques and explore remedies to mitigate the performance decline observed in some techniques under long-context.	contribution 0.99	0.78	0.17	speculative 0.62
s3	This is a course report for COS 598D Machine Learning and Systems by Prof.	background 0.89	0.09	0.01	asserted 0.91
s4	Kai Li at Princeton University.	background 0.95	0.04	0.01	unclear 0.63
s5	Due to limited computational resources, our experiments were conducted only on LLaMA-2-7B-32K.	limitation 1.00	0.92	0.02	asserted 1.00

What kind of paper the classifier reports this is: empirical study 1.00 · evidence from observation or experiment 0.97.

What was examined, and what was not

sources read

arXiv listing abstract v2

not examined

full text
figures and tables
references and citing sentences
relations to other papers

levels run

P abstract

not run

S sections
T body sentences
C citations
R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper

Original abstract

This study evaluates the effectiveness of zero-shot compression techniques on large language models (LLMs) under long-context. We identify the tendency for computational errors to increase under long-context when employing certain compression methods. We propose a hypothesis to explain the varied behavior of different LLM compression techniques and explore remedies to mitigate the performance decline observed in some techniques under long-context. This is a course report for COS 598D Machine Learning and Systems by Prof. Kai Li at Princeton University. Due to limited computational resources, our experiments were conducted only on LLaMA-2-7B-32K.

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What this paper does with the arXiv papers it cites

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

What it cites · 14 sentences without a confident label

Uses the method or tool of 2 papers

Due to its utilization of 'per-group' weight quantization and 'per-token' activation quantization, the method described in [ref] represents a robust quantization technique.

Evaluating Quantized Large Language Models · Empirical Evaluation · 0.95
Specifically, we choose two representative methods: magnitude pruning and Wanda [ref] .

A Simple and Effective Pruning Approach for Large Language Models · Related Works · 0.97

Cites as background 20 papers

On the other hand, researchers are also working on extending the context length of LLMs [ref] .

LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding · Introduction · 0.97
On the other hand, researchers are also working on extending the context length of LLMs [ref] .

LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression · Introduction · 0.97
* W8A8 quantization, where both activations and weights are quantized to INT8 [ref] . * Low-bit weight-only quantization (e.g., W4A16), where only weights are quantized into low-bit integers [ref] .

LUT-GEMM: Quantized Matrix Multiplication based on LUTs for Efficient Inference in Large-Scale Generative Language Models · Related Works · 0.95

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