Pith. sign in

REVIEW 6 major objections 5 minor 1 cited by

Enhancing Cache-Augmented Generation (CAG) with Adaptive Contextual Compression for Scalable Knowledge Integration

T0 review · 6 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper claims that Adaptive Contextual Compression reduces average context window occupancy by up to 45% and lets cache-augmented generation outperform both sparse and dense retrieval-augmented generation by 5-10% in BERTScore, turning…

desk verdict A plausible CAG-compression design whose experimental section does not support its headline numbers: missing benchmarks, an unmeasured occupancy claim, and a train/test ambiguity. read the letter →

arxiv 2505.08261 v1 pith:YUY5DT7Q submitted 2025-05-13 cs.CL cs.AI

Rishabh Agrawal , Himanshu Kumar This is my paper · ORCID
classification cs.CLcs.AI
keywords AdaptiveContextualCompressionCache-AugmentedGenerationRetrieval-Augmentedcontextmulti-hopreasoningBERTScorelow-latencyinferencehybridretrieval
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper argues that the main obstacle to Cache-Augmented Generation (CAG), namely limited context windows for large knowledge bases, can be removed by compressing the preloaded context adaptively. It introduces Adaptive Contextual Compression (ACC), a three-stage pipeline that scores snippets for relevance, summarizes them at multiple levels with BART, and uses a PPO-trained policy to decide what to prune or summarize under a token budget. The authors claim ACC reduces context window occupancy by up to 45 percent and that ACC-CAG beats sparse and dense RAG by 5-10 percent in BERTScore on HotPotQA and NaturalQuestions, while keeping inference latency under 700 ms. A Hybrid CAG-RAG framework adds selective retrieval only when a cache miss is detected, covering dynamic knowledge without sacrificing speed. If these results hold, CAG becomes a scalable, low-latency design for knowledge-intensive applications.

What carries the argument

The central mechanism is ACC, a three-module pipeline. Snippet Ranking trains a dual encoder (Dense Passage Retrieval or Sentence-BERT style) to score each snippet by cosine similarity to recent queries blended with an offline relevance estimate. Multi-Level Summarization arranges selected snippets into document, paragraph, and sentence tiers and runs a BART-based summarizer with a top-down relevance check, claiming up to 75 percent token reduction while preserving over 95 percent of task-critical content. Contextual Compression Policy Optimization casts compression as a Markov Decision Process in which a PPO-trained two-layer MLP chooses prune-or-summarize actions to maximize a reward blending downstream BERTScore with token cost. The Hybrid CAG-RAG framework wraps this in a cache-hit detector that triggers FAISS-based dense retrieval and re-compresses retrieved passages before appending them to the cached key-value store.

What would settle it

Compress a held-out knowledge base with ACC, then ask questions whose answers depend on facts that appear only in low-ranked snippets; if any gold fact is missing from the compressed context or the summary, the answer fails, directly testing whether the 95 percent content-preservation claim holds.

Watch

Extended reading notes

Core claim

The paper's central claim is that a three-component Adaptive Contextual Compression pipeline, composed of relevance scoring, multi-level BART summarization, and a PPO-trained compression policy, can compress the context preloaded into a cache-augmented generator so effectively that CAG scales to larger knowledge bases and improves answer quality relative to retrieval-based systems. On HotPotQA and NaturalQuestions, ACC-CAG is reported to outperform both sparse and dense RAG by 5-10 percent in BERTScore, reduce average context window occupancy by up to 45 percent, and cut peak GPU memory by 20-30 percent compared to standard CAG, while maintaining sub-700 ms inference times. The hybrid CAG-RAG framework further adds 1-2 BERTScore points at a 5-10 percent latency cost by triggering dense retrieval only on cache misses.

Load-bearing premise

The load-bearing premise is that the relevance scorer, BART summarizer, and PPO policy, fitted to the benchmark datasets, generalize to the test questions, and that abstractive summarization preserves the exact facts needed for answers.

Editorial extensions

If this is right

  • CAG with ACC becomes a viable low-latency alternative to RAG for large static knowledge stores, since it beats RAG in BERTScore while keeping inference under 700 ms.
  • Context window occupancy drops by up to 45 percent, letting the same 32k-token window hold 2-3 times more knowledge and cutting peak GPU memory by 20-30 percent.
  • The hybrid CAG-RAG framework covers dynamic knowledge by triggering retrieval only on cache misses, adding 1-2 BERTScore points at a 5-10 percent latency cost.
  • Incremental cache updates cut offline recompression time by 70 percent, easing deployment on frequently updated knowledge bases.
  • ACC's per-component ablations attribute 4, 2, and 3 BERTScore points on HotPotQA to relevance scoring, hierarchical summarization, and the RL policy respectively, implying each stage is load-bearing.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the gains generalize, adaptive compression may be a cheaper path to long-context performance than ever-expanding context windows, because much of a long context is redundant attention fodder.
  • The paper's 75 percent token reduction with 95 percent content-preservation claim is directly testable on out-of-domain documents by measuring whether gold answer spans survive compression.
  • The BERTScore advantage over RAG suggests that retrieval noise, rather than missing knowledge, is the main quality bottleneck in these benchmarks; ACC's edge should shrink on corpora where retrieval precision is already very high.
  • A natural next step, which the paper lists but does not evaluate, is a fairness-aware eviction policy that prevents systematic loss of low-frequency topics from the cache.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

6 major / 5 minor

Summary. The paper proposes Adaptive Contextual Compression (ACC), a three-stage pipeline (relevance scoring, multi-level BART-based summarization, and PPO-based compression policy) intended to make Cache-Augmented Generation (CAG) scalable to large and dynamic knowledge bases, together with a Hybrid CAG-RAG framework that selectively retrieves when the cache misses. The empirical section reports experiments on HotPotQA and NaturalQuestions, claiming that ACC-CAG outperforms sparse and dense RAG by 5-10% in BERTScore while reducing latency and memory, and that a hybrid variant adds further accuracy at modest cost. The abstract advertises four benchmark suites, but the experiments cover only two.

Significance. If the empirical claims were supported, the work would be a practically relevant contribution: adaptive compression of cached context is a plausible way to extend CAG beyond small static corpora, and the hybrid CAG-RAG design addresses a real limitation of static caching. The modular decomposition (relevance scoring, hierarchical summarization, policy-based compression) is reasonable and the paper clearly identifies the context-window bottleneck. However, the current evidence is not sufficient to establish the central claims. The evaluation lacks a stated train/test boundary for the learned components, the baselines use a different generator than the proposed method, the headline 45% occupancy reduction is never measured, and no code, data, or statistical uncertainty is provided. These issues are load-bearing because the paper's contribution is primarily empirical.

major comments (6)
  1. [Section IV.A.3] The implementation details state that the DPR dual-encoder is 'fine-tuned on each dataset's question-passage pairs,' the BART summarizer is 'trained separately at document, paragraph, and sentence granularities,' and the PPO policy is trained for 10k episodes, but the paper never reports whether Table I results are on held-out examples. If the same benchmark examples are used for training the compression components and for computing BERTScore, the claimed 5-10% advantage over RAG could be an in-sample artifact rather than a property of the method.
  2. [Section IV.A.2 vs. IV.A.3] The RAG baselines use a T5-large generator, while ACC-CAG and the hybrid use a '32k-token variant of the GPT-4 architecture' fine-tuned for QA. Comparing systems with different generators confounds the contribution of ACC with the choice of backbone. A controlled comparison with the same generator is required before the BERTScore differences in Table I can be attributed to compression.
  3. [Abstract and Section IV.A.1] The abstract promises evaluation on SQuAD v2, CNN/DailyMail, MultiWOZ, and HotpotQA, but Section IV.A.1 and Table I report only HotPotQA and NaturalQuestions. The claimed evidence for 'diverse datasets' and for scalability to 'large and dynamic knowledge bases' is therefore missing for three of the four promised suites, and NaturalQuestions is absent from the abstract's list.
  4. [Sections I and IV.B] The central efficiency claim that ACC 'reduces average context window occupancy by up to 45%' (abstract) is never measured. No experiment reports context-window occupancy, token counts, or a compression ratio; Table I contains only BERTScore, latency, and peak GPU memory, and the statement in Section IV.B.2 that ACC compresses '2-3x more content into a 32k-token window' is not supported by a direct occupancy metric.
  5. [Table I and Section IV.B.1] No error bars, standard deviations, number of runs, or significance tests are reported for any method. The claimed 5-10% BERTScore advantage and the 6-point HotPotQA margin cannot be distinguished from noise. Additionally, the 'approximately 5% F1' claim in Section III.A.3 is not supported by any F1 value in the experimental results.
  6. [Sections III and IV.A.3] No code or data are released, and several key hyperparameters are unspecified: the alpha in the relevance score, the top-k percentage of snippet retention, the number of retrieved passages m, and the cache-hit detector threshold. The PPO policy's reward function and token budget are also not described. This makes the empirical results unreproducible and prevents verification of the claimed policy improvements.
minor comments (5)
  1. [Abstract] The abstract sentence 'ACC reduces average context window occupancy by up to 45' is truncated; the percentage and the rest of the sentence are missing.
  2. [References] The bibliography contains duplicates: references [25], [28], [49], and [50] all cite the Longformer paper, and reference [33] duplicates reference [3]. The list should be cleaned.
  3. [Section III.A.2 vs. Abstract] The abstract describes 'dependency-aware sentence fusion and coreference resolution' as part of lossless compression, but the methodology section describes only a BART-based summarizer; the connection between these described techniques and the implemented system is unclear.
  4. [Section II.B] Claims about prior CAG results, such as 'cutting end-to-end latency by over 40%,' are presented without a citation or a specific source.
  5. [Figure 5] Figure 5, showing BERTScore comparisons, is placed after Table I but is never referenced in the text; please add a pointer in Section IV.B.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular dependency is demonstrable from the manuscript; the reported gains rest on an under-specified evaluation, not on a construction that equates prediction with input.

full rationale

ACC is an empirical pipeline rather than a formal derivation, and no quoted equation or procedure makes a reported quantity equal, by construction, to a fitted parameter. The closest candidate is the evaluation of Section IV: the DPR dual-encoder is 'fine-tuned on each dataset’s question–passage pairs' and the PPO policy is trained with rewards that 'blend downstream generation quality (e.g., BERTScore) with token-processing cost,' after which Table I reports BERTScore gains on HotPotQA and NaturalQuestions. But the manuscript nowhere states that the fine-tuning or policy training was performed on the same question–passage examples that are later scored in Table I; it simply omits the train/test split. Inferring an in-sample fit requires an assumption the text does not make, so this is a missing experimental detail and a correctness risk, not a demonstrated circular reduction. Similarly, the abstract's 'up to 45%' context-window occupancy reduction is never defined or reported in the experimental section, and the ACC-CAG backbone (a GPT-4 variant) differs from the T5-large generator used by the sparse and dense RAG baselines, which would confound the 5–10% BERTScore comparison. These are serious validity concerns, but they are not cases where a prediction is equivalent to its input by construction. There are no load-bearing self-citations: the paper cites external, unrelated prior work for DPR, BART, PPO, and baselines, and never invokes a uniqueness theorem or prior result authored by the present authors to force its design. Accordingly, the circularity score is 0.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

The central contribution is an engineering integration of known components. The ledger items are the trained or hand-chosen quantities and domain assumptions that the reported gains depend on. The main burden is that the learned components are fit to the evaluation datasets and that summary-based compression is assumed, not measured, to preserve answer facts.

free parameters (6)
  • alpha in relevance score = not reported
    Balances real-time query similarity against offline relevance in the score(s) formula (Section III.A.1); no value or tuning procedure is given.
  • top-k snippet retention percentage = not reported
    Determines how much context survives pruning in Snippet Ranking (Section III.A.1); the threshold is not disclosed.
  • PPO policy parameters = not reported
    Trained with BERTScore and token-cost rewards (Section III.A.3); this learned policy controls compression decisions and is central to the reported gains.
  • cache-hit detector threshold = not reported
    Controls whether retrieval is triggered in the Hybrid CAG-RAG framework (Section III.B.2); no classifier accuracy or threshold is reported.
  • number of retrieved passages m = not reported
    Top-m FAISS passages are merged into context after a cache miss (Section III.B.2); m is not specified.
  • BART summarizer weights at three granularities = not reported
    Summarizers are trained separately at document, paragraph, and sentence levels (Section III.A.2); no checkpoints or training details are given.
assumptions (5)
  • domain assumption Cosine similarity between query and snippet embeddings is a valid relevance signal.
    DPR and Sentence-BERT scores are treated as ground truth for snippet importance in Section III.A.1.
  • domain assumption Abstractive summarization preserves task-critical facts.
    Section III.A.2 claims 75% token reduction while preserving over 95% of task-critical content without showing a measurement of factual preservation.
  • domain assumption Attention and gradient saliency identify low-value tokens and segments.
    Token truncation and adaptive window allocation rely on saliency correlating with answer-bearing content (Sections III.A.3 and III.C.2).
  • domain assumption The RL policy trained with BERTScore reward generalizes across queries.
    PPO is trained on a reward combining generation quality and token cost; no evidence of out-of-distribution generalization is provided (Section III.A.3).
  • domain assumption Key-value cache reuse remains valid after cache updates and evictions.
    The hybrid framework appends new passages and evicts low-scoring segments while reusing precomputed key-value tensors; correctness of this operation is not analyzed (Section III.B.3).

how reviews work

0 comments
Cite this review

Pith. "Pith review of Enhancing Cache-Augmented Generation (CAG) with Adaptive Contextual Compression for Scalable Knowledge Integration." pith.science (2026). https://pith.science/paper/YUY5DT7Q

@misc{pith2026250508261,
  author       = {Pith},
  title        = {Pith review of: Enhancing Cache-Augmented Generation (CAG) with Adaptive Contextual Compression for Scalable Knowledge Integration},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YUY5DT7Q}},
  note         = {Machine review of arXiv:2505.08261}
}
read the original abstract

The rapid progress in large language models (LLMs) has paved the way for novel approaches in knowledge-intensive tasks. Among these, Cache-Augmented Generation (CAG) has emerged as a promising alternative to Retrieval-Augmented Generation (RAG). CAG minimizes retrieval latency and simplifies system design by preloading knowledge into the model's context. However, challenges persist in scaling CAG to accommodate large and dynamic knowledge bases effectively. This paper introduces Adaptive Contextual Compression (ACC), an innovative technique designed to dynamically compress and manage context inputs, enabling efficient utilization of the extended memory capabilities of modern LLMs. To further address the limitations of standalone CAG, we propose a Hybrid CAG-RAG Framework, which integrates selective retrieval to augment preloaded contexts in scenarios requiring additional information. Comprehensive evaluations on diverse datasets highlight the proposed methods' ability to enhance scalability, optimize efficiency, and improve multi-hop reasoning performance, offering practical solutions for real-world knowledge integration challenges.

Figures

Figures reproduced from arXiv: 2505.08261 by the authors.

Figure 1
Figure 1. Adaptive Contextual Compression (ACC) pipeline: (1) Snippet [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 3
Figure 3. Efficient Cache Management: (1) Incremental Updates, (2) Token [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Overall methodology: Adaptive Contextual Compression, Hierar [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figures from the paper (1 more)
Figure 5
Figure 5. Figure 5: BERTScore comparison across methods and datasets. [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ConceptBot: Enhancing Robot's Autonomy through Task Decomposition with Large Language Models and Knowledge Graph

    cs.RO 2025-08 conditional novelty 5.0 of 10

    Using ConceptNet-augmented prompts, ConceptBot reports 87% vs 31% success on implicit tasks and 76% vs 15% on risk-aware tasks over a re-implemented SayCan baseline, with an 80% SafeAgentBench score.

Reference graph

Works this paper leans on

48 extracted references · 42 canonical work pages · cited by 1 Pith paper

  1. [1]

    HotPotQA: A Dataset for Diverse, Explainable Multi- hop Question Answering,

    Z. Yang et al., “HotPotQA: A Dataset for Diverse, Explainable Multi- hop Question Answering,” in Proc. EMNLP, 2018

    work page 2018

  2. [2]

    NaturalQuestions: Benchmarking Question Answering Systems with Real User Queries,

    T. Kwiatkowski et al. , “NaturalQuestions: Benchmarking Question Answering Systems with Real User Queries,” Trans. Assoc. Comput. Linguistics, 2019

    work page 2019

  3. [3]

    Retrieval-Augmented Generation for Knowledge- Intensive NLP Tasks,

    P. Lewis et al. , “Retrieval-Augmented Generation for Knowledge- Intensive NLP Tasks,” in Advances in Neural Information Processing Systems, 2020

    work page 2020

  4. [4]

    Dense Passage Retrieval for Open-Domain Question Answering,

    J. Karpukhin et al. , “Dense Passage Retrieval for Open-Domain Question Answering,” in Proc. EMNLP, 2020

    work page 2020

  5. [5]

    Billion-Scale Similarity Search with GPUs,

    J. Johnson, M. Douze, and H. J ´egou, “Billion-Scale Similarity Search with GPUs,” IEEE Trans. Big Data , 2019

    work page 2019

  6. [6]

    Transformers: State-of-the-Art Natural Language Processing,

    T. Wolf et al. , “Transformers: State-of-the-Art Natural Language Processing,” in Proc. EMNLP (System Demonstrations) , 2020

    work page 2020

  7. [7]

    GPT-4 Technical Report,

    OpenAI, “GPT-4 Technical Report,” 2023

    2023

  8. [8]

    BART: Denoising Sequence-to-Sequence Pre- Training for Natural Language Generation, Translation, and Compre- hension,

    M. Lewis et al. , “BART: Denoising Sequence-to-Sequence Pre- Training for Natural Language Generation, Translation, and Compre- hension,” in Proc. ACL, 2020

    work page 2020

Show all 48 references
  1. [9]

    Proximal Policy Optimization Algorithms,

    J. Schulman et al., “Proximal Policy Optimization Algorithms,” arXiv preprint arXiv:1707.06347, 2017

    2017 arXiv

  2. [10]

    Saliency-Guided Dynamic Pruning for Efficient Inference,

    A. Agarwal et al. , “Saliency-Guided Dynamic Pruning for Efficient Inference,” in Proc. ICLR, 2024

    2024

  3. [11]

    Some Methods for Classification and Analysis of Multivariate Observations,

    J. MacQueen, “Some Methods for Classification and Analysis of Multivariate Observations,” in 5th Berkeley Symp. Math. Statist. Prob., 1967

    1967

  4. [12]

    Latent Dirichlet Allocation,

    D. M. Blei, A. Y . Ng, and M. I. Jordan, “Latent Dirichlet Allocation,” J. Mach. Learn. Res. , 2003

    2003

  5. [13]

    RAPTOR: Hierarchical Compression and Retrieval for Multi-Hop Question Answering,

    P. Sarthi et al. , “RAPTOR: Hierarchical Compression and Retrieval for Multi-Hop Question Answering,” in Proc. NAACL, 2024

    2024

  6. [14]

    Neural Cache-Hit Detection for Language Model Augmentation,

    X. Wang, Y . Li, and Z. Chen, “Neural Cache-Hit Detection for Language Model Augmentation,” in Proc. AAAI, 2023

    2023

  7. [15]

    Adaptive Context Updates in Generative Transformers,

    L. Xu and H. Zhang, “Adaptive Context Updates in Generative Transformers,” in ICLR Workshop on Efficient LLMs , 2023

    2023

  8. [16]

    Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks,

    N. Reimers and I. Gurevych, “Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks,” in Proc. EMNLP, 2019

    2019

  9. [17]

    MetaSummary: Cluster-Based Abstractive Sum- maries for Multi-Hop Question Answering,

    S. Mombaerts et al., “MetaSummary: Cluster-Based Abstractive Sum- maries for Multi-Hop Question Answering,” in Proc. EMNLP, 2024

    2024

  10. [18]

    Extractive Segment Selection for Context Compression in LLMs,

    W. Shen, J. Li, and Y . Zhao, “Extractive Segment Selection for Context Compression in LLMs,” in Proc. EMNLP, 2024

    2024

  11. [19]

    RECOMP: Contrastive Learning for Extractive Summarization in Question Answering,

    X. Xu, R. Kumar, and P. Li, “RECOMP: Contrastive Learning for Extractive Summarization in Question Answering,” in Proc. NAACL, 2024

    2024

  12. [20]

    TokenPrune: Gradient-Based Token Pruning for Transformers,

    Z. Zhang et al. , “TokenPrune: Gradient-Based Token Pruning for Transformers,” in Proc. ICML, 2024

    2024

  13. [21]

    Filtering Irrelevant Context for Large Language Models: A Survey,

    R. Verma and J. Lee, “Filtering Irrelevant Context for Large Language Models: A Survey,” IEEE Trans. Knowledge Data Eng. , 2024

    2024

  14. [22]

    Lost-in-the-Middle: Attention Decay in Long Transformer Inputs,

    S. Liu, X. Zhao, and Y . Tan, “Lost-in-the-Middle: Attention Decay in Long Transformer Inputs,” in Proc. EMNLP, 2023

    2023

  15. [23]

    Cache-Augmented Generation for LLMs,

    X. Huang et al. , “Cache-Augmented Generation for LLMs,” arXiv preprint arXiv:2301.00001, 2023

    2023 arXiv

  16. [24]

    Long Context RAG Performance of Large Language Models,

    Q. Leng et al., “Long Context RAG Performance of Large Language Models,” arXiv preprint arXiv:2411.03538 , 2024

    2024 arXiv

  17. [25]

    Reformer: The Efficient Transformer,

    N. Kitaev, Ł. Kwiatkowski, and A. Patel, “Reformer: The Efficient Transformer,” in Proc. ICLR, 2020

    2020

  18. [27]

    Compressive Transformers for Long-Range Sequence Modeling,

    R. Child et al., “Compressive Transformers for Long-Range Sequence Modeling,” arXiv preprint arXiv:1911.05507 , 2019

    1911 arXiv

  19. [29]

    The ‘Middle-of-Context’ Phenomenon in Long-Context Language Models,

    F. Liu et al., “The ‘Middle-of-Context’ Phenomenon in Long-Context Language Models,” in Proc. ACL, 2024

    2024

  20. [30]

    Self-Route: Dynamic Routing Between Parametric and Retrieval Paths in LLMs,

    P. Li et al. , “Self-Route: Dynamic Routing Between Parametric and Retrieval Paths in LLMs,” in Proc. ICML, 2024

    2024

  21. [31]

    A Survey on Retrieval-Augmented Generation,

    X. Gao, Y . Li, and H. Wang, “A Survey on Retrieval-Augmented Generation,” ACM Comput. Surveys , 2023

    2023

  22. [32]

    AIGC: Architectures for Knowledge- Enhanced Language Models,

    Y . Zhao and A. Kumar, “AIGC: Architectures for Knowledge- Enhanced Language Models,” in NeurIPS Workshop on Knowledge in LLMs, 2024

    2024

  23. [33]

    Retrieval-Augmented Generation for Knowledge- Intensive NLP Tasks,

    M. Lewis et al. , “Retrieval-Augmented Generation for Knowledge- Intensive NLP Tasks,” in Advances in Neural Information Processing Systems, 2020

    2020

  24. [34]

    Benchmarking Retrieval-Augmented Generators on Open-Domain QA,

    D. Chen et al. , “Benchmarking Retrieval-Augmented Generators on Open-Domain QA,” in Proc. EMNLP, 2023

    2023

  25. [35]

    SQuAD: 100,000+ Questions for Machine Com- prehension of Text,

    P. Rajpurkar et al., “SQuAD: 100,000+ Questions for Machine Com- prehension of Text,” in Proc. EMNLP, 2016

    2016

  26. [36]

    RAFT: Retrieval-Augmented Fine- Tuning for Robust QA,

    L. Zhang, M. Singh, and P. Kohli, “RAFT: Retrieval-Augmented Fine- Tuning for Robust QA,” in Proc. ICML, 2024

    2024

  27. [37]

    RALM: Retrieval-Augmented Language Models with Precomputed KV Caches,

    J. Hu and Q. Lu, “RALM: Retrieval-Augmented Language Models with Precomputed KV Caches,” in Proc. ICLR, 2024

    2024

  28. [38]

    Robustness of Large Language Models to Retrieval Noise,

    N. Kandpal et al., “Robustness of Large Language Models to Retrieval Noise,” in Proc. AAAI, 2023

    2023

  29. [39]

    TurboRAG: Accelerating Retrieval-Augmented Genera- tion with Precomputed KV Caches,

    S. Lu et al., “TurboRAG: Accelerating Retrieval-Augmented Genera- tion with Precomputed KV Caches,” arXiv preprint arXiv:2404.56789, 2024

    2024

  30. [40]

    Graph-Based Indexing for Efficient Retrieval- Augmented Generation,

    S. Delile et al. , “Graph-Based Indexing for Efficient Retrieval- Augmented Generation,” in Proc. SIGIR, 2024

    2024

  31. [41]

    GNN-RAG: Graph Neural Network Enhanced Retrieval-Augmented Generation,

    D. Mavromatis et al., “GNN-RAG: Graph Neural Network Enhanced Retrieval-Augmented Generation,” in Proc. KDD, 2024

    2024

  32. [42]

    Self-Reflective Retrieval-Augmented Generation for Improved Factual Consistency,

    A. Asai et al. , “Self-Reflective Retrieval-Augmented Generation for Improved Factual Consistency,” arXiv preprint arXiv:2311.67890 , 2023

    2023

  33. [43]

    CommunityKG-RAG: Knowledge Graph-Augmented Retrieval-Augmented Generation,

    H. Chang et al., “CommunityKG-RAG: Knowledge Graph-Augmented Retrieval-Augmented Generation,” arXiv preprint arXiv:2405.23456 , 2024

    2024

  34. [44]

    ANN at Scale: Accelerating Similarity Search in Large- scale Vector Databases,

    Y . Guo et al., “ANN at Scale: Accelerating Similarity Search in Large- scale Vector Databases,” Proc. VLDB Endow., 2023

    2023

  35. [45]

    REALM: Retrieval-Augmented Language Model Pre-Training,

    G. Izacard and E. Grave, “REALM: Retrieval-Augmented Language Model Pre-Training,” in Proc. ICLR, 2021

    2021

  36. [46]

    Cache-Augmented Generation for Domain-Specific QA,

    W. Huang et al., “Cache-Augmented Generation for Domain-Specific QA,” in Proc. AAAI, 2023

    2023

  37. [47]

    Extended-Context Transformers for Multi-Hop QA,

    T. Leng, A. Kumar, and J. Smith, “Extended-Context Transformers for Multi-Hop QA,” in Proc. ACL, 2024

    2024

  38. [48]

    Attention Is All You Need,

    A. Vaswani et al., “Attention Is All You Need,” in Advances in Neural Information Processing Systems , 2017

    2017

  39. [49]

    Reformer: The Efficient Transformer,

    N. Kitaev et al., “Reformer: The Efficient Transformer,” inProc. ICLR, 2020

    2020

  40. [50]

    Longformer: The Long-Document Transformer,

    I. Beltagy et al. , “Longformer: The Long-Document Transformer,” arXiv preprint arXiv:2004.05150 , 2020

    2004 arXiv

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.