arxiv: 2604.26209 · v1 · submitted 2026-04-29 · 💻 cs.CL · cs.AI

Recognition: unknown

Breaking the Autoregressive Chain: Hyper-Parallel Decoding for Efficient LLM-Based Attribute Value Extraction

Theodore Glavas , Nikhita Vedula , Dushyanta Dhyani , Yilun Zhu , Shervin Malmasi

Authors on Pith no claims yet

Pith reviewed 2026-05-07 13:28 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords hyper-parallel decodingattribute value extractionLLM inference optimizationparallel token generationautoregressive decodingconditional independencebatch efficiency

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The pith

Hyper-Parallel Decoding generates multiple independent sequences from one LLM prompt at once.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper presents Hyper-Parallel Decoding to speed up language model inference for tasks like attribute value extraction that require several independent outputs from the same document context. Standard sequential decoding processes one token after another, but the method treats attribute-value pairs as conditionally independent so their generations can run in parallel inside a prompt and even across stacked documents. It achieves the parallelism by manipulating position IDs to support out-of-order token production while sharing memory and computation across batches. If correct, the approach cuts both inference time and costs by up to 13.8 times with no loss in output quality and works on any LLM.

Core claim

Hyper-Parallel Decoding is a decoding algorithm that accelerates offline inference by leveraging shared memory and computation across batches and by enabling out-of-order token generation through position ID manipulation. In attribute value extraction, conditional independence of attribute-value pairs permits parallel value generation within each prompt. By further stacking multiple documents within a single prompt, up to 96 tokens can be decoded in parallel. The method reduces inference costs and total inference time by up to 13.8X without compromising output quality and applies to all LLMs with no domain-specific assumptions.

What carries the argument

Hyper-Parallel Decoding, which uses position ID manipulation to permit out-of-order token generation while batching independent sequences to share memory and computation.

If this is right

Offline attribute value extraction at industry scale can reduce costs by hundreds of thousands of dollars.
The method applies unchanged to any existing LLM.
A single prompt can decode up to 96 tokens in parallel by stacking documents.
Output quality matches standard autoregressive decoding.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same out-of-order generation approach could extend to other tasks with independent outputs, such as multi-fact extraction or simultaneous classifications from one text.
Serving systems for offline batches might adopt position ID manipulation as a default optimization.
Further tests could measure how many documents can be stacked before context limits or quality degrade.

Load-bearing premise

Attribute-value pairs extracted from the same document are conditionally independent so that one generation does not affect another.

What would settle it

If side-by-side tests on an AVE benchmark show that standard sequential decoding produces higher accuracy or different values than HPD on a meaningful fraction of cases, the independence premise would be falsified.

Figures

Figures reproduced from arXiv: 2604.26209 by Dushyanta Dhyani, Nikhita Vedula, Shervin Malmasi, Theodore Glavas, Yilun Zhu.

**Figure 1.** Figure 1: AVE on the e-commerce domain: Given a set of attributes for the category Television, values for each attribute are autoregressively generated using an LLM. Our method, Hyper-Parallel Decoding (HPD) parallelizes the extraction of values within the prompt to dramatically increase throughput and decrease cost. of generative LLMs limits their adoption in realworld scenarios (Zhang et al., 2025). Prior work ha… view at source ↗

**Figure 2.** Figure 2: (a) An illustration of the generated output with HPD. The skeleton output defines the attributes to view at source ↗

**Figure 3.** Figure 3: Illustration of the attention mask between queries Q (rows) and keys K (columns). Grey boxes indicate view at source ↗

**Figure 4.** Figure 4: Throughput (products / s) increase from HPD view at source ↗

**Figure 6.** Figure 6: Position ID assignment algorithm for the view at source ↗

**Figure 5.** Figure 5: The basic Hyper-Parallel Decoding algorithm. view at source ↗

**Figure 7.** Figure 7: Alternative block diagram for the HPD process: At step view at source ↗

**Figure 8.** Figure 8: Amazon Reviews 2023 dataset: Matrix of win (W), tie (T) and loss (L) rate of GPT-4.1, standard Qwen3-8B (AR), and Qwen3-8B with HPD. Wins, ties and losses are determined by human annotators randomly comparing 2/3 model outputs for one product, and judging which set of values are most faithful to the product context. Performance score is calculated as (W+T)/(T+L) normalized to 0-100 view at source ↗

**Figure 9.** Figure 9: Amazon Reviews Prompts You are an expert at comparing products to determine if they are equivalent in terms of function, specification, form, design, material, quantity, quality, brand value. Analyze the <product> elements below, and output a list of price-sensitive attributes that experienced customers and product category experts would consider when comparing these products to see if they are exact equiv… view at source ↗

**Figure 10.** Figure 10: Attribute definition prompt for Claude 3.7 Sonnet view at source ↗

**Figure 11.** Figure 11: Evaluation prompt used for Claude 3.5 Sonnet view at source ↗

read the original abstract

Some text generation tasks, such as Attribute Value Extraction (AVE), require decoding multiple independent sequences from the same document context. While standard autoregressive decoding is slow due to its sequential nature, the independence between output sequences offers an opportunity for parallelism. We present Hyper-Parallel Decoding, a novel decoding algorithm that accelerates offline decoding by leveraging both shared memory and computation across batches. HPD enables out-of-order token generation through position ID manipulation, significantly improving efficiency. Experiments on AVE show that attribute-value pairs are conditionally independent, enabling us to parallelize value generation within each prompt. By further stacking multiple documents within a single prompt, we can decode in parallel up to 96 tokens per prompt. HPD works with all LLMs, and reduces both inference costs and total inference time by up to 13.8X without compromising output quality, potentially saving hundreds of thousands of dollars on industry AVE tasks. Although designed for attribute extraction, HPD makes no assumptions unique to the AVE domain and can in theory be applied to other scenarios with independent output structures.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

HPD's position ID manipulation for parallel independent generations is a practical batch optimization, but the 'works with all LLMs' claim looks shaky without checks on relative encodings.

read the letter

The punchline is that this paper offers a decoding optimization for LLM tasks with independent output sequences by using position ID manipulation to allow out-of-order generation and document stacking for more parallelism. It targets efficiency in offline attribute value extraction and reports substantial speedups. What the paper does well is identify a practical opportunity in AVE where attribute values don't depend on each other, allowing parallelization within a prompt. Stacking multiple documents in one prompt to decode up to 96 tokens at once is a nice multiplier. The claim of up to 13.8X reduction in inference costs and time without quality loss would be valuable for industry if it holds, as it addresses real deployment costs. The method is presented as an algorithmic change rather than a fitted model, which keeps it straightforward. The soft spots are around generality and evidence. The assertion that HPD works with all LLMs seems overstated given how positional encodings differ. Models with relative position mechanisms like RoPE could see altered attention when positions are reassigned across sequences, which might affect output quality even if the sequences are independent. The abstract doesn't mention tests on such models or provide ablations, error bars, or statistical tests, so the quality preservation is hard to assess fully. The experiments being AVE-specific also means the 'no unique assumptions' part needs more support from other domains. The citation pattern isn't an issue here since it's a new algorithm. This paper is for practitioners in NLP and ML engineering who handle high-volume structured extraction with LLMs and are looking for inference optimizations. It would be useful for reading groups focused on efficient decoding methods. I would recommend sending it to peer review. The efficiency gains are worth verifying properly, and the core idea is clear enough to benefit from referee feedback on the architectural claims and experimental rigor.

Referee Report

2 major / 1 minor

Summary. The paper proposes Hyper-Parallel Decoding (HPD), a novel decoding algorithm for LLM-based Attribute Value Extraction (AVE) that exploits conditional independence among attribute-value pairs to enable out-of-order token generation via position ID manipulation. This allows parallel decoding both within individual prompts and across stacked documents, claiming up to 13.8X reductions in inference time and cost while preserving output quality, with asserted applicability to all LLMs and other tasks featuring independent output structures.

Significance. If the core claims hold under rigorous validation, HPD could deliver substantial practical impact for industrial-scale AVE and similar structured extraction workloads by reducing LLM inference costs, while the underlying technique of positional manipulation for parallel independent sequences represents a potentially useful addition to the toolkit for efficient offline decoding.

major comments (2)

[Abstract] Abstract: The claim that HPD 'works with all LLMs' and preserves quality is load-bearing for the central contribution but rests on an unverified assumption about positional encodings. Models using relative encodings such as RoPE (Llama, Mistral) tie rotary embeddings to relative distances; reassigning position IDs for out-of-order generation can alter attention scores and logits, risking quality degradation even when conditional independence holds.
[Abstract] Abstract and experiments section: The assertion that attribute-value pairs are conditionally independent (enabling parallelism) is presented without error bars, ablation studies, statistical significance tests, or cross-model comparisons, leaving the quality-preservation claim difficult to evaluate and the 13.8X speedup claim unsupported by reproducible evidence.

minor comments (1)

The description of the HPD algorithm would benefit from explicit pseudocode or a diagram showing how position IDs are manipulated across stacked documents and within prompts.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback on our manuscript. We address each major comment below with clarifications based on our experimental setup and indicate the specific revisions we will make.

read point-by-point responses

Referee: [Abstract] Abstract: The claim that HPD 'works with all LLMs' and preserves quality is load-bearing for the central contribution but rests on an unverified assumption about positional encodings. Models using relative encodings such as RoPE (Llama, Mistral) tie rotary embeddings to relative distances; reassigning position IDs for out-of-order generation can alter attention scores and logits, risking quality degradation even when conditional independence holds.

Authors: We agree that the broad phrasing 'works with all LLMs' requires qualification, particularly for relative positional encodings like RoPE. Our experiments were performed on Llama-family models (which use RoPE), and we observed no measurable quality degradation in attribute-value extraction when using HPD compared to standard autoregressive decoding. This suggests that our position-ID manipulation preserves intra-sequence relative distances for each independent output while avoiding problematic cross-sequence interactions in the attention computation. In the revised manuscript we have (1) tempered the abstract claim to 'HPD is compatible with LLMs using both absolute and relative positional encodings, as validated on RoPE-based models', (2) added a short methodological paragraph explaining the RoPE compatibility argument, and (3) included a brief Mistral result for additional coverage. These are targeted presentation changes rather than new experiments. revision: partial
Referee: [Abstract] Abstract and experiments section: The assertion that attribute-value pairs are conditionally independent (enabling parallelism) is presented without error bars, ablation studies, statistical significance tests, or cross-model comparisons, leaving the quality-preservation claim difficult to evaluate and the 13.8X speedup claim unsupported by reproducible evidence.

Authors: We accept that the current presentation would benefit from stronger statistical grounding. In the revised manuscript we have added: error bars (standard deviation across 5 random seeds) to all reported F1 and latency figures; an ablation that directly measures output divergence between parallel HPD and forced sequential decoding to quantify the conditional-independence assumption; paired statistical significance tests (Wilcoxon signed-rank) confirming that quality differences are not significant; and expanded cross-model tables. For the 13.8X speedup claim we now include per-component timing breakdowns and will release the full evaluation code and prompts upon acceptance to support reproducibility. These additions directly respond to the request for more rigorous validation. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical algorithmic speedup

full rationale

The paper introduces Hyper-Parallel Decoding as a new decoding algorithm that uses position ID manipulation to enable out-of-order parallel generation of independent sequences. The central claims of up to 13.8X efficiency gains without quality loss rest on empirical experiments demonstrating conditional independence of attribute-value pairs in AVE tasks, followed by direct measurement of inference time and cost on stacked prompts. No equations, fitted parameters, or self-citations are invoked that would reduce the speedup result to a tautology or to the input data by construction. The independence observation is presented as an experimental finding rather than an assumption smuggled in via prior self-work, and the 'works with all LLMs' statement is framed as an empirical observation rather than a derived uniqueness theorem. The derivation chain is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the domain assumption that attribute values are conditionally independent and that position ID changes preserve model behavior for out-of-order generation.

axioms (1)

domain assumption Attribute-value pairs are conditionally independent
Explicitly invoked to justify parallel value generation within and across prompts.

invented entities (1)

Hyper-Parallel Decoding (HPD) no independent evidence
purpose: Algorithm for parallel out-of-order token generation via position ID manipulation
New decoding procedure introduced by the paper.

pith-pipeline@v0.9.0 · 5503 in / 1146 out tokens · 41560 ms · 2026-05-07T13:28:04.471945+00:00 · methodology

discussion (0)

Reference graph

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correct", CN=

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