Unveiling Knowledge Utilization Mechanisms in LLM-based Retrieval-Augmented Generation

HaiFeng Wang; Hua Wu; Jing Liu; Ruiyang Ren; Wayne Xin Zhao; Yucheng Wang; Yuhao Wang

REVIEW 5 major objections 5 minor 1 cited by

Unveiling Knowledge Utilization Mechanisms in LLM-based Retrieval-Augmented Generation

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

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Pith's one-line read This paper claims RAG knowledge use in LLMs proceeds through four stages — refinement, elicitation, expression, contestation — steered by passage relevance and rebalanced by deactivating KAPE-identified neurons.

desk verdict A plausible and useful map of how RAG models mix parametric and retrieved knowledge, with a real neuron-level control result—but the four-stage decomposition rests on an underspecified key-token definition and unnormalized attention sums. read the letter →

arxiv 2505.11995 v1 pith:JIT7QKCV submitted 2025-05-17 cs.CL

Yuhao Wang , Ruiyang Ren , Yucheng Wang , Wayne Xin Zhao , Jing Liu , Hua Wu , Haifeng Wang This is my paper · ORCID

classification cs.CL

keywords Retrieval-AugmentedGenerationKnowledgeUtilizationLargeLanguageModelsStreamingKnowledge-SpecificNeuronsOpen-DomainQuestionAnsweringInterpretabilityConflict

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

Retrieval-augmented generation is usually treated as plugging retrieved text into a prompt, but this paper asks what the model actually does with that text. By tracing attention and saliency flows between four token groups — the retrieved context, the answer-bearing key tokens inside it, the query, and the answer prompt — the authors find that external knowledge passes through four distinct layer-wise stages: refinement, elicitation, expression, and contestation. The relevance of the retrieved passage to the query steers this stream, and the decisive gate sits at the elicitation stage, where the model decides whether to absorb the external information into the query representation. At the neuron level, the paper introduces KAPE, an entropy measure that identifies neurons dedicated to internal or external knowledge, and shows that deactivating the most knowledge-specific of them shifts the model's reliance between the two sources. If the claims hold, RAG is not a monolithic lookup but a staged, partly steerable pipeline.

What carries the argument

The key machinery is a set of three attention-based information-flow metrics — $IF^{{kc}}$, $IF^{{kq}}$, $IF^{{ka}}$ — summed over heads and token pairs between the key group K (the answer-bearing tokens extracted from the context) and the context C, query Q, and answer prompt A, respectively, complemented by gradient saliency matrices $IF^{{kc}}$_s, $IF^{{kq}}$_s, $IF^{{ka}}$_s. The phase offsets of these curves across layers define the four stages. On the neuron side, KAPE (knowledge activation probability entropy) computes, for each MLP neuron, an entropy over the L1-normalized activation probabilities for internal versus external knowledge; low KAPE marks a knowledge-specific neuron, and setting its activation to zero is the intervention that shifts reliance between the two sources. Residual-stream and early-decoding unembedding logits are then used to attribute contributions to MHA versus MLP modules, showing that MLP tracks the fine-grained factual correctness of external knowledge.

What would settle it

Hold the token layout fixed but destroy the passage's meaning (for example, shuffle its sentences or substitute the answer-bearing words with random tokens) and recompute the three information-flow curves. If the key-to-query peak at the elicitation layers and the four-stage phase pattern persist undiminished, the flows are tracking attention geometry rather than knowledge, and the stage decomposition would be refuted.

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Extended reading notes

Core claim

The central discovery is that knowledge utilization in LLM-based RAG is a staged process, not a single integration step. Across transformer layers, the flow of attention from answer-bearing key tokens to the context peaks early and then fades (refinement); the flow from key to the query rises next and then falls (elicitation); the flow from key to the answer prompt rises in the middle layers and falls in the final quarter (expression); and in the deepest layers the external-knowledge signal declines while the gradient tendency to strengthen it increases, indicating competition with internal memory (contestation). The paper further shows that passage relevance governs this stream mainly at the elicitation stage: when key-to-query attention is causally masked there, the probability shift for external-knowledge answers is largest for highly relevant passages. Introducing KAPE, the paper identifies the top 1% lowest-entropy neurons as internal-knowledge or external-knowledge specialists and demonstrates that deactivating them measurably changes factual accuracy in the expected direction on Natural Questions, TriviaQA, and HotpotQA, in both LLaMA and Qwen models.

Load-bearing premise

The analysis assumes that attention and gradient scores between token groups faithfully trace genuine knowledge transfer, and that the answer-bearing words inside the retrieved passage are cleanly extracted; if those scores reflect token counts or positional patterns rather than knowledge, the four-stage decomposition and the relevance-guided streaming results do not follow.

Editorial extensions

If this is right

RAG is not a monolithic lookup: knowledge flows from the context into the query and then to the answer in a layer-wise sequence, so interventions that target the elicitation stage should be the most effective way to control whether retrieved evidence is used.
Passage relevance acts mostly at the elicitation stage; a model given irrelevant passages stops integrating external knowledge early in the pipeline, which explains why retrieval quality has an outsized effect on RAG accuracy.
Because low-KAPE neurons are dedicated to one knowledge source, editing the top 1% of them yields targeted shifts in reliance: deactivating external-knowledge neurons hurts factual accuracy with gold documents but helps when documents are noisy, while the reverse holds for internal-knowledge neurons.
MLP layers are the module that verifies fine-grained factual correctness of external knowledge, since gold versus fake passage differences appear there but not in MHA layers, pointing to MLP as the place to inspect for hallucination from noisy context.
The four-stage pattern and the KAPE control both replicate across LLaMA and Qwen families and across single- and multi-hop QA datasets, so the mechanisms are general rather than model-specific.

Reading between the lines

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

Editorial inference: the same four-stage lens could be applied to other context-augmented settings such as in-context learning or long-context reasoning, where the 'key' group would be prompt exemplars or relevant spans; the paper's metrics do not depend on retrieval being the source of external tokens.
Editorial inference: KAPE's entropy criterion suggests a testable recipe for building controllable RAG without fine-tuning — a sparse mask or steering vector over low-entropy neurons could be tuned to balance knowledge sources on new domains, something the paper demonstrates only for the deactivation direction.
Editorial inference: because the saliency analysis shows that the decline in key-to-answer flow in deep layers is accompanied by an increasing gradient tendency to strengthen it, a plausible reading is that internal knowledge actively suppresses external knowledge in the contestation stage; a direct follow-up would test whether removing internal-knowledge neurons in that stage specifically restore

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Key thing to know: this is the first systematic attempt I've seen to trace how RAG models mix parametric and retrieved knowledge through the layers, and the KAPE neuron-targeting result looks real. But the four-stage story is built on a token-classification step that is never actually defined, so the stage boundaries should be treated as suggestive rather than established.

What's genuinely new: the four-stage decomposition (refinement, elicitation, expression, contestation), the KAPE metric for finding internal-vs-external knowledge neurons, and the deactivation experiments showing that knocking out those neurons shifts which source dominates. The gold/fake passage contrast and the early-decoding analysis add converging evidence, and the cross-model (LLaMA, Qwen) and cross-dataset (NQ, TriviaQA, HotpotQA) consistency is a real point in its favor.

Where it gets soft: the whole information-flow analysis depends on a token group called K, "the potential answers extracted by the LLM from the context," and the extraction procedure is never specified. Every IF curve in Figure 1—and therefore every stage boundary—is a sum of attention weights from K tokens to C, Q, or A tokens. If K is defined differently (generated answer string vs. gold span matched back), the shapes change. And Eq. (4) is a raw sum, not normalized by the number of token pairs, so longer or shorter key spans could create rise-and-fall patterns that have nothing to do with knowledge transfer. That is a load-bearing gap.

Also, KAPE selects neurons using the same RAG-vs-closed-book contrast that the deactivation experiment then measures, so part of the confirmation is baked into the selector. The cross-dataset transfer (neurons chosen on NQ work on TriviaQA and HotpotQA) is a good partial answer to that, but it isn't a full one. No error bars or random-neuron baselines are reported, and the four-stage boundaries are drawn by eye. None of this kills the central claim, but it means the paper is a strong hypothesis-generation result, not a confirmed mechanism.

Who it's for: anyone working on RAG interpretability or knowledge conflicts. It deserves a proper peer review, but the authors should be asked to define K operationally, normalize the IF metrics, and add random-neuron and shuffled-token baselines. As it stands, I'd trust the KAPE deactivation effect more than I'd trust the four-stage boundaries.

Referee Report

5 major / 5 minor

Summary. This paper presents a mechanistic study of how LLMs use internal (parametric) and external (retrieved) knowledge in retrieval-augmented open-domain QA. Using attention-based information-flow (IF) metrics defined over four token groups (context C, key K, query Q, and answer prompt A), the authors observe layer-wise trends and propose a four-stage decomposition of knowledge utilization: refinement, elicitation, expression, and contestation (Sec. 3.2). They further report that passage relevance steers the knowledge elicitation stage, corroborate the stages with gradient-based saliency and a causal-mask intervention, and support the relevance-guided account empirically (Secs. 3.3-3.4). At the module level, the paper introduces Knowledge Activation Probability Entropy (KAPE) to identify neurons associated with internal versus external knowledge, and shows that deactivating these neurons shifts QA accuracy on Natural Questions, TriviaQA, and HotpotQA across LLaMA-3-8B, LLaMA-3-70B, and Qwen-2.5-1.5B (Table 2). Finally, unembedding-logit analyses are used to argue that MLP layers verify the accuracy of external knowledge while MHA layers integrate information from multiple sources (Sec. 4.2). The headline claims are that RAG knowledge integration proceeds in identifiable stages and that it can be causally controlled at the neuron level.

Significance. If the results hold, the paper delivers a rare combination of information-flow and causal, neuron-level evidence about RAG, spanning two model families (LLaMA and Qwen), several scales, and three datasets, with KAPE deactivation effects transferring across datasets in directionally consistent ways (deactivating external-knowledge neurons hurts more under gold documents and can improve accuracy under noisy documents). The intervention experiments in Sec. 4.1 go beyond correlational attention analysis, and the cross-model consistency in Sec. 3.2 suggests the four-stage phenomenology is not an artifact of one architecture. The paper is also explicit about its claims, which makes them testable. However, the headline macro-level decomposition rests entirely on the IF definitions in Eq. (4), whose key-token group is not defined operationally and whose aggregation is not normalized; until those definitions are pinned down, the stage boundaries and relevance comparisons cannot be independently verified. The novelty is also partially incremental relative to the cited knowledge-conflict literature (refs. [12, 13] in the text).

major comments (5)

[Sec. 3.1, Eqs. (3)-(4)] The token group K, which is the source for every information-flow metric, is never defined operationally. Section 3.1 states only that K denotes 'the potential answers extracted by the LLM from the context, obtained following Equation (3)', but Eq. (3) merely defines a_ext = LLM(I, q, P) and does not specify how the answer is aligned back into the passage tokens, whether exact string matching is used, how multi-token or paraphrased answers are handled, or which occurrence is selected when the answer appears multiple times. Because K is the source group X in IF^KC_a, IF^KQ_a, and IF^KA_a in Eq. (4), every stage boundary in Figures 1 and 3 and every relevance comparison inherits this ambiguity. Please provide a precise extraction-and-alignment procedure, including worked examples, so that the curves are reproducible and falsifiable.
[Sec. 3.1, Eq. (4)] The IF metrics are raw sums of attention weights over all token pairs, with no normalization for the sizes of the groups X and Y. The size of the key group changes across the experimental conditions by construction: the fake passages replace the correct answer with an incorrect string of different length (Sec. 2.2), and the relevance conditions in Sec. 3.4.1 use passages of different lengths and different answer spans. A raw sum can therefore vary in magnitude purely because of token counts, and the claims that gold and fake passage patterns are 'largely consistent' (Sec. 3.2.1) and that decreasing relevance makes the flows 'less noticeable' (Sec. 3.4.2) need to be re-examined with normalized metrics (e.g., mean attention weight per directed token pair) or with length-matched baselines.
[Sec. 3.2.2] The four stage boundaries are identified by visual inspection of the layer-wise curves in Figure 1, and Figure 1 reports only the average over three datasets with no variance or significance information. Since the four-stage decomposition is the paper's central macroscopic claim, the authors should provide a quantitative boundary-detection rule (e.g., extrema or change points of the averaged IF curves), a robustness check across random subsets of questions, and confidence bands. Without such a rule, later claims such as 'relevance primarily affects the knowledge elicitation stage' (Sec. 3.4) are not well-defined, because the elicitation stage's layer span itself is determined by eyeballing the same curves.
[Secs. 4.1.1-4.1.2, Table 2] The KAPE identification pipeline leaves several load-bearing details unspecified: the threshold used to retain 'neurons with significant activation probabilities' is never given a value, the top-1% selection ratio is a free parameter with no sensitivity analysis, and the number of questions used in the deactivation experiments underlying Table 2 is not reported. Table 2 also reports no error bars or significance tests, so the differences between the 'Deactivate IK' and 'Deactivate EK' rows (e.g., average EM drops of -6.57 versus -12.64 on gold documents for LLaMA-3-8B) cannot be assessed for statistical reliability. Please report the identification settings, the evaluation sample sizes, and bootstrap or standard-error estimates for the main comparisons.
[Secs. 3.3 and 4.1.1] Two of the corroborations share the very contrast they are meant to verify. KAPE selects neurons by comparing activation probabilities between the RAG and closed-book settings, and the deactivation experiment then measures the effect of ablating these neurons on exactly that internal-versus-external contrast; the saliency corroboration in Sec. 3.3 uses Eq. (5), which is a gradient-weighted version of the same attention matrices that define the stages. The cross-dataset transfer of the KAPE neurons (identified on NQ, evaluated on TriviaQA and HotpotQA) partially mitigates the first concern, but the paper should still demonstrate that the attribution has content beyond the selection signal, for example by comparing against control neuron sets matched on overall activation frequency or by using an independent localization method such as causal tracing with different interventions.

minor comments (5)

[Fig. 1 and Sec. 3.2.1] The caption of Figure 1 says the figure shows 'two versions of LLaMA-3 with different parameter scales', while the text claims consistency across LLaMA-3-8B, LLaMA-3.1-8B, LLaMA-3-70B, Qwen-2.5-1.5B and Qwen-2.5-7B; please state explicitly which models are plotted and which are only discussed verbally.
[Figures 1, 3, 4, 7 and Eq. (8)] Several figures and one equation contain glyph/font artifacts in the review copy (the '/uni00000013...' sequences in figure labels and the garbled expectation/norm symbols in Eq. (8)); the camera-ready version must ensure that all formula and axis-label text renders with a proper math font.
[Secs. 3.2.1 and 3.3] The refinement stage is defined in Sec. 3.2 through key-to-context flow (IF^KC_a), but the saliency discussion in Sec. 3.3 describes the model as 'increas[ing] the flows from the context to the key'; the direction of the proposed interaction should be stated consistently for each stage.
[Sec. 4.1.1, Eq. (8)] The definition of the activation probability p^k_{i,j} is difficult to parse; please rewrite it in standard notation and clarify whether p^IK_{i,j} and p^EK_{i,j} are computed over the same question set in the closed-book and RAG conditions.
[Sec. 3.4.3, Eq. (6)] It is not immediately clear why masking key-to-query attention increases the probability of generating a_ext for higher-relevance passages; a brief explanation of the sign of d in Eq. (6) would make the heatmap in Figure 5 much easier to interpret.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central results are empirical layer-wise measurements and causal interventions, not consequences of the paper's definitions.

full rationale

The paper's derivation chain is largely self-contained. Internal and external knowledge are operationalized in Eqs. (1)-(3), and the information-flow metrics in Eq. (4) are sums over attention/saliency entries. The four stages in Sec. 3.2.2 are read off the measured layer curves, so the taxonomy could have failed to emerge if the flows had not risen and fallen in the observed order. The saliency corroboration in Sec. 3.3 uses a gradient-weighted variant of the attention matrix; although related, it is a distinct estimator and provides an additional consistency check rather than a definitional restatement. The relevance-guided streaming claim in Sec. 3.4 is tested by a causal masking intervention in Eq. (6), not merely by correlation. The KAPE procedure in Sec. 4.1.1 selects neurons by activation-probability contrast between closed-book and RAG settings, while the deactivation experiments in Sec. 4.1.2 measure downstream factual accuracy under gold and noisy documents, and the results transfer to held-out datasets (TriviaQA, HotpotQA) and additional model families. The selection contrast does not logically entail the observed performance shifts, so this is correlational selection plus causal validation, not a fitted parameter renamed as a prediction. The references to [29] from the same research group provide definitions and passage-relevance categories, but the central mechanistic conclusions do not reduce to that citation. The main validity concern is the under-specified operational definition of the 'key' token group in Sec. 3.1 and the unnormalized sums in Eq. (4); that is a verifiability and control problem, not circularity, because no equation is shown to be equivalent to another by construction.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claims rest on interpretive assumptions (attention equals knowledge flow, closed-book versus RAG equals internal versus external) and on hand-chosen analysis thresholds (top-1% KAPE selection, an unspecified activation threshold, and visually inferred stage boundaries). No new physical entities are introduced. These assumptions are not formalized or tested against baselines, so they carry the main epistemic burden.

free parameters (3)

KAPE low-entropy selection ratio = top 1% of neurons
Section 4.1.1 chooses the top 1% lowest-KAPE neurons; no sensitivity analysis is reported, and this fraction determines which neurons are labeled knowledge-specific.
Knowledge-specific neuron activation threshold = not reported
Section 4.1.1 applies an unspecified threshold on activation probabilities after selecting the top 1%; it directly determines which neurons are deactivated.
Four-stage layer boundaries = model-dependent, not reported
Section 3.2.2 delineates stages from visual inspection of information-flow curves; boundary layers are not estimated or reported, so any reimplementation must re-fit them.

assumptions (4)

domain assumption Attention scores directly reflect information flow between tokens.
Section 3.1 uses attention-matrix sums (Eq. 4) as quantitative knowledge-stream metrics; this equates attention weight with knowledge transfer, a debated stance in interpretability.
domain assumption Gradient saliency computed as attention times gradient (Eq. 5) measures the marginal effect of information flow.
Used in Section 3.3 as corroboration; it relies on local Taylor approximation and access to a supervised fine-tuning loss L_sft on the open-domain QA task.
domain assumption Closed-book answers are pure internal knowledge and RAG answers with gold passages are manifestations of external knowledge.
Section 2.1 defines internal and external knowledge operationally; however RAG outputs also involve parametric knowledge, so the clean separation is assumed.
domain assumption Context, key, query, and answer-prompt token groups can be cleanly separated, with key tokens bearing the answer extracted from context.
Section 3.1 defines components C, K, Q, A but does not specify how key tokens are extracted; all information-flow metrics depend on this segmentation.

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Pith. "Pith review of Unveiling Knowledge Utilization Mechanisms in LLM-based Retrieval-Augmented Generation." pith.science (2026). https://pith.science/paper/JIT7QKCV

@misc{pith2026250511995,
  author       = {Pith},
  title        = {Pith review of: Unveiling Knowledge Utilization Mechanisms in LLM-based Retrieval-Augmented Generation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JIT7QKCV}},
  note         = {Machine review of arXiv:2505.11995}
}

read the original abstract

Considering the inherent limitations of parametric knowledge in large language models (LLMs), retrieval-augmented generation (RAG) is widely employed to expand their knowledge scope. Since RAG has shown promise in knowledge-intensive tasks like open-domain question answering, its broader application to complex tasks and intelligent assistants has further advanced its utility. Despite this progress, the underlying knowledge utilization mechanisms of LLM-based RAG remain underexplored. In this paper, we present a systematic investigation of the intrinsic mechanisms by which LLMs integrate internal (parametric) and external (retrieved) knowledge in RAG scenarios. Specially, we employ knowledge stream analysis at the macroscopic level, and investigate the function of individual modules at the microscopic level. Drawing on knowledge streaming analyses, we decompose the knowledge utilization process into four distinct stages within LLM layers: knowledge refinement, knowledge elicitation, knowledge expression, and knowledge contestation. We further demonstrate that the relevance of passages guides the streaming of knowledge through these stages. At the module level, we introduce a new method, knowledge activation probability entropy (KAPE) for neuron identification associated with either internal or external knowledge. By selectively deactivating these neurons, we achieve targeted shifts in the LLM's reliance on one knowledge source over the other. Moreover, we discern complementary roles for multi-head attention and multi-layer perceptron layers during knowledge formation. These insights offer a foundation for improving interpretability and reliability in retrieval-augmented LLMs, paving the way for more robust and transparent generative solutions in knowledge-intensive domains.

Figures

Figures reproduced from arXiv: 2505.11995 by the authors.

**Figure 2.** Illustrations of the four stages of knowledge stream [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** Attention-based information flow based on external passages of various relevance with the query. [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: Saliency score on three information flow directions [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

**Figure 6.** Figure 6: Cases for the LLM’s knowledge utilization tenden [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

**Figure 7.** Figure 7: Unembedded logits of internal and external knowl [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]

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Ruiyang Ren, Yingqi Qu, Jing Liu, Wayne Xin Zhao, Qiaoqiao She, Hua Wu, Haifeng Wang, and Ji-Rong Wen. 2021. RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language P...

2021
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Ruiyang Ren, Yuhao Wang, Junyi Li, Jinhao Jiang, Wayne Xin Zhao, Wenjie Wang, and Tat-Seng Chua. 2025. Holistically Guided Monte Carlo Tree Search for Intricate Information Seeking. arXiv preprint arXiv:2502.04751 (2025)

2025 arXiv
[29]

Ruiyang Ren, Yuhao Wang, Yingqi Qu, Wayne Xin Zhao, Jing Liu, Hao Tian, Hua Wu, Ji-Rong Wen, and Haifeng Wang. 2023. Investigating the factual knowledge boundary of large language models with retrieval augmentation. arXiv preprint arXiv:2307.11019 (2023)

2023 arXiv
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Noam Shazeer. 2020. Glu variants improve transformer. arXiv preprint arXiv:2002.05202 (2020)

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Tianyi Tang, Wenyang Luo, Haoyang Huang, Dongdong Zhang, Xiaolei Wang, Xin Zhao, Furu Wei, and Ji-Rong Wen. 2024. Language-Specific Neurons: The Key to Multilingual Capabilities in Large Language Models. In Proceedings of the 62nd Annual Meeting of the Association for Computat...

2024
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Xinyu Tang, Xiaolei Wang, Zhihao Lv, Yingqian Min, Wayne Xin Zhao, Binbin Hu, Ziqi Liu, and Zhiqiang Zhang. 2025. Unlocking General Long Chain-of- Thought Reasoning Capabilities of Large Language Models via Representation Engineering. arXiv preprint arXiv:2503.11314 (2025)

2025 arXiv
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Xinyu Tang, Xiaolei Wang, Wayne Xin Zhao, Siyuan Lu, Yaliang Li, and Ji-Rong Wen. 2024. Unleashing the Potential of Large Language Models as Prompt Optimizers: Analogical Analysis with Gradient-based Model Optimizers. arXiv preprint arXiv:2402.17564 (2024)

2024 arXiv
[34]

Xinyu Tang, Xiaolei Wang, Wayne Xin Zhao, and Ji-Rong Wen. 2024. Dawn- icl: Strategic planning of problem-solving trajectories for zero-shot in-context learning. arXiv preprint arXiv:2410.20215 (2024)

2024 arXiv
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Surat Teerapittayanon, Bradley McDanel, and Hsiang-Tsung Kung. 2016. Branchynet: Fast inference via early exiting from deep neural networks. In 2016 23rd international conference on pattern recognition (ICPR) . IEEE, 2464–2469

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Lean Wang, Lei Li, Damai Dai, Deli Chen, Hao Zhou, Fandong Meng, Jie Zhou, and Xu Sun. 2023. Label words are anchors: An information flow perspective for understanding in-context learning. arXiv preprint arXiv:2305.14160 (2023)

2023 arXiv
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Yuhao Wang, Ruiyang Ren, Junyi Li, Wayne Xin Zhao, Jing Liu, and Ji-Rong Wen. 2024. REAR: A Relevance-Aware Retrieval-Augmented Framework for Open-Domain Question Answering. arXiv preprint arXiv:2402.17497 (2024)

2024 arXiv
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Jun Xiao, Hao Ye, Xiangnan He, Hanwang Zhang, Fei Wu, and Tat-Seng Chua
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An Yang, Baosong Yang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Zhou, Cheng- peng Li, Chengyuan Li, Dayiheng Liu, Fei Huang, et al. 2024. Qwen2 technical report. arXiv preprint arXiv:2407.10671 (2024)

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Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 2018. HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language...

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2023 arXiv
[2017]

arXiv preprint arXiv:1708.04617 (2017)

Attentional factorization machines: Learning the weight of feature interac- tions via attention networks. arXiv preprint arXiv:1708.04617 (2017)

2017 arXiv

Pith tools

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

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Jasmijn Bastings and Katja Filippova. 2020. The elephant in the interpretability room: Why use attention as explanation when we have saliency methods? arXiv preprint arXiv:2010.05607 (2020)

arXiv 2020

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2020

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Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. 2017. Reading Wikipedia to Answer Open-Domain Questions. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . Association for Computational Linguistics

work page 2017

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Xiaoxue Cheng, Junyi Li, Wayne Xin Zhao, and Ji-Rong Wen. 2025. Think More, Hallucinate Less: Mitigating Hallucinations via Dual Process of Fast and Slow Thinking. arXiv preprint arXiv:2501.01306 (2025)

arXiv 2025

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Xiaoxue Cheng, Junyi Li, Wayne Xin Zhao, Hongzhi Zhang, Fuzheng Zhang, Di Zhang, Kun Gai, and Ji-Rong Wen. 2024. Small Agent Can Also Rock! Empowering Small Language Models as Hallucination Detector.arXiv preprint arXiv:2406.11277 (2024)

work page Pith review arXiv 2024

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Zican Dong, Junyi Li, Jinhao Jiang, Mingyu Xu, Wayne Xin Zhao, Bingning Wang, and Weipeng Chen. 2025. LongReD: Mitigating Short-Text Degradation of Long-Context Large Language Models via Restoration Distillation. arXiv preprint arXiv:2502.07365 (2025)

arXiv 2025

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Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. 2024. The llama 3 herd of models. arXiv preprint arXiv:2407.21783 (2024)

arXiv 2024

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Nelson Elhage, Neel Nanda, Catherine Olsson, Tom Henighan, Nicholas Joseph, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, Nova Das- Sarma, Dawn Drain, Deep Ganguli, Zac Hatfield-Dodds, Danny Hernandez, Andy Jones, Jackson Kernion, Liane Lovitt, Kamal Ndousse, Dario Amodei, Tom Brown, Jack Clark, Jared Kaplan, Sam McCandlish, and Chris Olah....

2021