Question Answering under Temporal Conflict: Evaluating and Organizing Evolving Knowledge with LLMs

Atahan \"Ozer; \c{C}a\u{g}atay Y{\i}ld{\i}z

REVIEW 3 major objections 5 minor 19 references

Question Answering under Temporal Conflict: Evaluating and Organizing Evolving Knowledge with LLMs

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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

Pith's one-line read A lightweight agent that files facts as timestamped quadruples in an external memory answers questions about evolving knowledge more accurately than in-context learning or retrieval-augmented generation on two new benchmarks.

desk verdict A useful pair of temporal-QA benchmarks and a plausible structured-memory method, undercut by a claim that its own tables contradict. read the letter →

arxiv 2506.07270 v1 pith:YLQ4SFKL submitted 2025-06-08 cs.CL

Atahan Özer , Çağatay Y{i}ld{i}z This is my paper

classification cs.CL

keywords temporalquestionansweringknowledgeconflictorganizationretrieval-augmentedgenerationin-contextlearningWikiUnifiedClarkLLMevaluation

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

The paper claims that large language models are unreliable when the evidence in context contains multiple, time-stamped versions of the same fact, and that this can be fixed without retraining. The authors introduce two benchmarks, Temporal Wiki and Unified Clark, that simulate facts drifting across Wikipedia snapshots and accumulating across news articles. They then propose knowledge organization, a lightweight agentic method that extracts (subject, relation, object, timestamp) quadruples from source documents into a structured external memory, and retrieves only the entries matching the question's subject and relation when answering. Across both benchmarks and several models, this scheme outperforms in-context learning and retrieval-augmented generation, with the largest gains on list-type questions that require integrating conflicting facts. A sympathetic reader would care because the method offers a cheap, training-free alternative to continually updating a model's parametric knowledge.

What carries the argument

The load-bearing object is the structured knowledge base assembled by the knowledge organization agent. The agent decomposes a question into a (subject, relation, object, timestamp) quadruple, extracts all facts in the evidence document about that subject and relation using in-context learning, and stores them as entries indexed by subject and relation. At inference time only the retrieved entries are used to formulate the answer, so the model never sees raw, conflicting documents. The benchmarks are the second piece of machinery: Temporal Wiki pairs Templama-style triplets with historical Wikipedia snapshots under three evidence regimes (closest, latest, cumulative), and Unified Clark concatenates timestamped news articles to force the model to enumerate every time-dependent instance of a fact.

What would settle it

Score a random sample of Temporal Wiki and Unified Clark answers with human annotators who see the model outputs without knowing which scheme produced them; if the knowledge-organization advantage over RAG shrinks or reverses under blinded human scoring, the reported gain is an evaluation artifact.

Watch

Extended reading notes

Core claim

The central discovery is that structuring temporally conflicting evidence before answering is more effective than feeding raw or retrieved text to the model. Using a small in-context extraction step, the agent converts each incoming document into explicit (subject, relation, object, timestamp) entries, stores them indexed by subject and relation, and at answer time retrieves only the matching entries. On questions where the model already knew the answer zero-shot, this organization causes the least degradation when conflicting context is added; on questions the model initially got wrong, it produces the largest improvement. The paper reads these results as evidence that distilling dense, time-varying documents into concise, structured facts is a better use of the model's reasoning than either long-context in-context learning or chunk-level retrieval.

Load-bearing premise

The accuracy comparison depends on trusting LLM judges to score open-ended answers, and the paper reports no dataset-specific agreement study showing the judges do not favor the structured outputs of knowledge organization; the RAG hyperparameter search also lacks a described validation split.

Editorial extensions

If this is right

On Temporal Wiki, knowledge organization yields the best accuracy on both the zero-shot-correct and zero-shot-wrong question splits, followed by RAG and then ICL.
On Unified Clark, where answers must enumerate all time-dependent instances, knowledge organization outperforms both baselines by a larger margin, e.g., GPT-4o Mini goes from 0.47 (ICL) and 0.48 (RAG) to 0.69.
Both ICL and RAG degrade when given the latest snapshot instead of the temporally closest one, suggesting that excessive temporal context injects noise.
Zero-shot accuracy rises with the frequency of fact changes, consistent with frequently updated pages appearing more often in training data.
Knowledge organization does not require retraining, so it can incorporate new documents as they arrive without updating model weights.

Reading between the lines

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

Editorial inference: The same extraction-and-index loop could be applied to non-QA tasks such as entity-centric summarization or timeline generation, where the structured memory would provide an explicit notion of what changed when.
Editorial inference: If the advantage persists under human scoring, knowledge organization offers a practical route for keeping deployed assistants current by appending new documents to the structured memory instead of fine-tuning, a regime the paper does not explicitly test.
Editorial inference: The method's dependence on in-context extraction suggests its gain will shrink for relations that are rarely stated verbatim in the source text; testing on relations with low surface-form frequency would bound the approach's scope.
Editorial inference: Because the benchmarks are English and Wikipedia/news-centric, the temporal-conflict effect on other domains, such as legal or biomedical fact drift, remains an open question that could be tested by reusing the same pipeline.

Share X Bluesky LinkedIn Reddit HN

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

Two things to know about arXiv:2506.07270. First, the Temporal Wiki benchmark is a genuinely new and useful assembly: it repurposes Templama questions and pairs them with historical Wikipedia snapshots to simulate factual drift. Unified Clark is a smaller extension, but together they give the community a testbed for an active problem. Second, the paper's main selling point—that Knowledge Organization (KO) 'consistently outperforms' ICL and RAG—doesn't hold for the condition that matters most, and the tables as printed don't support it.

What the paper does well: the problem framing is clear, KO is a simple extract-store-query pipeline that is easy to reproduce, and Temporal Wiki is public. The idea of organizing temporally conflicting documents into structured fact entries is sensible and worth testing.

The soft spots are real. In Table 2 (questions the model got wrong zero-shot), RAG Closest beats KO Closest for all three models: 0.78 vs 0.74, 0.73 vs 0.66, 0.72 vs 0.68. In Table 1, RAG Closest also beats KO Closest for the 8B and 7B models (0.86 vs 0.83 and 0.86 vs 0.81), and for the 70B it is 0.92 vs 0.90. So Section 3.1.1's claim of consistent KO superiority is false unless the tables are misaligned—and if they are, that's a transcription problem that must be fixed. Worse, the promised 'Cumulative' column is absent from the tables, so we can't check whether the claim rests on that condition. That is load-bearing.

Beyond that, the empirical work lacks statistical discipline: no confidence intervals or significance tests, and some differences are tiny (0.90 vs 0.92). The LLM judges are cited as reliable in general, but no dataset-specific agreement is reported, despite the appendix promising it. And RAG hyperparameters were chosen via an 'exhaustive ablation search' with no validation split described—that is how you get overfitting, not a method.

Who should read this: anyone building temporal QA benchmarks or working on updating LLM knowledge without retraining. The benchmarks are likely to be reused, but the method itself is not field-redefining. My recommendation: send it to peer review, but expect a major revision. The authors need to reconcile the prose with the tables, add uncertainty quantification, and report judge reliability on these specific tasks. If those are fixed, this could be a solid benchmark contribution. As it stands, I would not cite it yet.

Referee Report

3 major / 5 minor

Summary. The paper introduces two benchmarks for temporal question answering under knowledge conflict (Temporal Wiki and Unified Clark) and proposes a lightweight agentic 'knowledge organization' (KO) approach that extracts (subject, relation, object, timestamp) quadruples into an external structured memory, then reasons over the temporally filtered entries. Experiments on three LLMs with ICL, RAG, and KO report that KO improves accuracy over ICL and RAG, especially on list-type and conflict-heavy questions. The claims of consistent superiority, however, appear to be contradicted by the numbers in the tables, and the evaluation relies on LLM judges without dataset-specific reliability analysis.

Significance. If the central claim holds, the paper contributes a practical, training-free method for adapting LLMs to evolving knowledge, plus two benchmarks that could be reused. The emphasis on temporal conflict and list aggregation is a useful extension of prior work on temporal QA and knowledge editing. However, the paper's primary empirical claim needs to be accurately restated and supported by proper statistical and reliability analyses before the contribution can be assessed. The benchmarks and the KO pipeline are potentially valuable resources for the community.

major comments (3)

[Section 3.1.1] The sentence 'Our knowledge organization (KO) approach consistently outperforms both ICL and RAG across all models, except for the RAG setup using Llama 3.1 70B' is not supported by the tables as printed. In Table 2 (questions whose zero-shot answers are wrong), the RAG Closest condition exceeds the KO Closest condition for all three models: Llama-3.1 70B (0.78 vs 0.74), Llama-3 8B (0.73 vs 0.66), and Mistral 7B (0.72 vs 0.68). In Table 1, RAG Closest also beats or ties KO Closest for Llama-3 8B (0.86 vs 0.83), Mistral 7B (0.86 vs 0.81), and Llama-3.1 70B (0.92 vs 0.92). The abstract's general statement that the method 'outperforms ICL and RAG baselines across both benchmarks' is therefore too strong. Either the column alignment is a transcription error that must be corrected, or the empirical claim must be revised to specify the conditions under which KO is actually superior (e.g., the Cumulative condition or the Unified Clark setting).
[Section 2.3] The paper says that 'Further details on the evaluation setup and reliability analysis are provided in the Appendix,' but the Appendix contains only a data format description. No dataset-specific agreement statistics are reported for the LLM-based evaluators (e.g., agreement between Llama 3.1 70B and Qwen-2 72B judges, or a human-validated subsample). Since the central accuracy comparison relies on these judges, and since KO produces structured outputs that might bias the judges, the missing reliability analysis is a load-bearing gap. The paper needs to provide judge-judge agreement on a sample of each benchmark, or a human evaluation subset, to show that the reported accuracy differences are not artifacts of the evaluation protocol.
[Section 2.3] The RAG hyperparameters (500-character chunks, 50-character overlap, top-12 retrieval, 512-dimensional embeddings) are said to be obtained 'after exhaustive ablation search,' but the paper does not describe the search procedure, the criterion used, or whether the search was performed on a validation split separate from the test set. If the same questions used for evaluation were also used for hyperparameter selection, the RAG baseline may be overfit to the test set, which would distort the comparison with KO. Please describe the validation split and the exact search protocol.

minor comments (5)

[Tables 1-2] The column headers list three KO subcolumns (Closest, Latest, Cumulative) but each row in Table 2 contains only six numbers, leaving the column alignment ambiguous. This should be fixed to make the reported values unambiguous.
[Section 2.3 / Table 3] The model names are inconsistent: Section 2.3 mentions 'Qwen-2 72B', while Table 3 lists 'Qwen 3.1 70B'. Please unify the model naming.
[Appendix] The appendix begins with the boilerplate sentence 'You may include other additional sections here.', which should be removed before submission.
[Section 2.2.2] The description of Unified Clark says it is 'accessible through (Li et al., 2024a)', but no direct link or download URL is given; adding the link would help reproducibility.
[References] References Dhingra et al. (2022a) and Dhingra et al. (2022b) appear to be the same paper (same title and DOI); please cite once and use the other key consistently.

Circularity Check

0 steps flagged · score 0.0 of 10

No notable circularity: KO's results are not constructed from its inputs; remaining issues are correctness risks.

full rationale

The paper's derivation chain is self-contained against external datasets. Temporal Wiki is built from Templama Wikipedia snapshots with a GPT-4o answer-support filter (Sec 2.2.1), and Unified Clark concatenates ERASE news articles (Sec 2.2.2); neither benchmark defines the target accuracy in terms of KO. The KO agent extracts (subject, relation, object, timestamp) quadruples into a structured memory and answers from that memory (Sec 2.4); the accuracy numbers are empirical measurements, not identities. The only author self-citation, Yıldız et al. (2024), appears in a background sentence about continual pretraining and is not load-bearing. Two limitations are worth flagging as correctness risks rather than circularity: (1) RAG hyperparameters are said to be "obtained after exhaustive ablation search" without specifying the split, which could imply test-set tuning; and (2) LLM judges, one of which (Llama 3.1 70B) is also an evaluated model, could carry systematic bias, and no dataset-specific agreement study is reported. Separately, the Sec 3.1.1 statement that KO "consistently outperforms both ICL and RAG across all models, except for the RAG setup using Llama 3.1 70B" is contradicted by Table 2's Closest columns for all models; this is an internal-consistency/correctness problem, not a circular dependency. No prediction in the paper reduces by construction to a fitted input or to a self-citation chain.

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

The empirical claims rely on several tuned hyperparameters and unvalidated assumptions. No new physical or conceptual entities are introduced; the structured knowledge base is a software component, not an entity with independent evidence.

free parameters (4)

RAG chunk size = 500 characters
Chosen after exhaustive ablation search (Section 2.3); no held-out split reported.
RAG chunk overlap = 50 characters
Selected with the chunk size during the same ablation search.
RAG top-k = 12 chunks
Retrieval hyperparameter tuned without a described validation split.
RAG embedding dimension / encoder = 512-dimensional
Embedding size stated, but the underlying encoder model is unnamed, so this cannot be independently reproduced.

assumptions (3)

domain assumption LLM-as-judge reliably scores open-ended answers on these benchmarks
Section 2.3 adopts consensus voting between Llama 3.1 70B and Qwen-2 72B, citing prior literature agreement, but reports no human-validated agreement on this dataset.
domain assumption Wikipedia snapshot from the year following the query time contains the answer
Dataset construction in Section 2.2.1 uses the earliest snapshot after the query year, with fallback logic that assumes temporal coverage.
domain assumption GPT-4o semantic verification correctly filters documents
Used to confirm the reference supports the answer (Section 2.2.1); no audit of false positives or false negatives is reported.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Question Answering under Temporal Conflict: Evaluating and Organizing Evolving Knowledge with LLMs." pith.science (2026). https://pith.science/paper/YLQ4SFKL

@misc{pith2026250607270,
  author       = {Pith},
  title        = {Pith review of: Question Answering under Temporal Conflict: Evaluating and Organizing Evolving Knowledge with LLMs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YLQ4SFKL}},
  note         = {Machine review of arXiv:2506.07270}
}

read the original abstract

Large language models (LLMs) exhibit remarkable capabilities in question answering and reasoning thanks to their extensive parametric memory. However, their knowledge is inherently limited by the scope of their pre-training data, while real-world information evolves continuously. Updating this knowledge typically requires costly and brittle re-training, or in-context learning (ICL), which becomes impractical at scale given the volume and volatility of modern information. Motivated by these limitations, we investigate how LLMs perform when exposed to temporal text corpora, or documents that reflect evolving knowledge over time, such as sports biographies where facts like a player's "current team" change year by year. To this end, we introduce two new benchmarks: Temporal Wiki, which captures factual drift across historical Wikipedia snapshots, and Unified Clark, which aggregates timestamped news articles to simulate real-world information accumulation. Our analysis reveals that LLMs often struggle to reconcile conflicting or outdated facts and can be misled when multiple versions of a fact appear in context. To address these issues, we propose a lightweight, agentic framework that incrementally builds a structured, external memory from source documents without requiring re-training. This knowledge organization strategy enables models to retrieve and reason over temporally filtered, relevant information at inference time. Empirically, our method outperforms ICL and RAG baselines across both benchmarks, especially on questions requiring more complex reasoning or integration of conflicting facts.

Figures

Figures reproduced from arXiv: 2506.07270 by the authors.

**Figure 2.** Visual summary of our agentic workflow, where incoming text documents are turned into entries in a [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** Accuracy plotted against how many times the [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

19 extracted references · 6 canonical work pages

[1]

Language models are few-shot learners

Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901,

work page 1901
[5]

Albert Q

URLhttps://arxiv.org/ abs/2110.03215. Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, L´elio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timoth´ee Lacroix, and William El S...

arXiv
[6]

GrowOVER: How Can LLMs Adapt to Growing Real-World Knowledge?

URL https://arxiv.org/abs/2406.05606. 9 Evgenii Kortukov, Alexander Rubinstein, Elisa Nguyen, and Seong Joon Oh. Studying large language model behav- iors under realistic knowledge conflicts,

work page Pith review arXiv
[7]

Quinn Leng, Jacob Portes, Sam Havens, Matei Zaharia, and Michael Carbin

URL https://arxiv.org/abs/2404.16032. Quinn Leng, Jacob Portes, Sam Havens, Matei Zaharia, and Michael Carbin. Long context and rag performance in large language models. https://www.databricks.com/blog/ long-context-rag-performance-llms , August

arXiv
[8]

Mosh Levy, Alon Jacoby, and Yoav Goldberg

Mosaic AI Research, Databricks, Accessed: 2024-09-02. Mosh Levy, Alon Jacoby, and Yoav Goldberg. Same task, more tokens: the impact of input length on the reasoning performance of large language models. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar (eds.), Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1...

work page 2024
[9]

URL https: //aclanthology.org/2024.acl-long.818

Association for Computational Linguistics. URL https: //aclanthology.org/2024.acl-long.818. Belinda Z. Li, Emmy Liu, Alexis Ross, Abbas Zeitoun, Graham Neubig, and Jacob Andreas. Language modeling with editable external knowledge, 2024a. URL https://arxiv.org/abs/2406.11830. Tianle Li, Ge Zhang, Quy Duc Do, Xiang Yue, and Wenhu Chen. Long-context llms str...

arXiv 2024
[10]

URL https://aclanthology.org/2024

doi: 10.1162/tacl a 00638. URL https://aclanthology.org/2024. tacl-1.9. Adam Liˇska, Tom´aˇs Koˇcisk´y, Elena Gribovskaya, Tayfun Terzi, Eren Sezener, Devang Agrawal, Cyprien de Mas- son d’Autume, Tim Scholtes, Manzil Zaheer, Susannah Young, Ellen Gilsenan-McMahon, Sophia Austin, Phil Blunsom, and Angeliki Lazaridou. Streamingqa: A benchmark for adaptatio...

doi:10.1162/tacl 2024
[11]

Kelvin Luu, Daniel Khashabi, Suchin Gururangan, Karishma Mandyam, and Noah A

URL https://arxiv.org/abs/2205.11388. Kelvin Luu, Daniel Khashabi, Suchin Gururangan, Karishma Mandyam, and Noah A. Smith. Time waits for no one! analysis and challenges of temporal misalignment. In Marine Carpuat, Marie-Catherine de Marneffe, and Ivan Vladimir Meza Ruiz (eds.), Proceedings of the 2022 Conference of the North American Chapter of the Assoc...

arXiv 2022

Show all 19 references

[12]

doi: 10.18653/v1/2022.naacl-main.435

Association for Computational Linguistics. doi: 10.18653/v1/2022.naacl-main.435. URL https://aclanthology.org/2022.naacl-main.435. Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, Shas...

2022 doi
[14]

Chain of thought prompting elicits reasoning in large language models

10 Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H Chi, Quoc Le, and Denny Zhou. Chain of thought prompting elicits reasoning in large language models. arXiv preprint arXiv:2201.11903,

arXiv
[15]

ISBN 9781713871088

Curran Associates Inc. ISBN 9781713871088. Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cis- tac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, C...

2020
[17]

URL https://aclanthology.org/ 2024.acl-long.563

Association for Computational Linguistics. URL https://aclanthology.org/ 2024.acl-long.563. Ca˘gatay Yıldız, Nishaanth Kanna Ravichandran, Prishruit Punia, Matthias Bethge, and Beyza Ermis. Investigating continual pretraining in large language models: Insights and implications,

2024
[18]

Yifan Zhang, Yang Yuan, and Andrew Chi-Chih Yao

URL https://arxiv.org/ abs/2402.17400. Yifan Zhang, Yang Yuan, and Andrew Chi-Chih Yao. Meta prompting for ai systems,

arXiv
[19]

Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P

URL https:// arxiv.org/abs/2311.11482. Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P. Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. Judging llm-as-a-judge with mt- bench and chatbot arena. I...

arXiv
[20]

event_id

Curran Associates Inc. 11 A Appendix You may include other additional sections here. Temporal Wiki Data Format The dataset is structured in JSON format. Each entry in the dataset represents a specific event and contains historical data related to that event at different points...

2010
[2020]

doi: 10.18653/v1/2020.emnlp-demos.6

Association for Computational Linguistics. doi: 10.18653/v1/2020.emnlp-demos.6. URL https://aclanthology.org/2020.emnlp-demos.6. Jian Xie, Kai Zhang, Jiangjie Chen, Renze Lou, and Yu Su. Adaptive chameleon or stubborn sloth: Revealing the behavior of large language models in k...

2020 doi
[2021]

doi: 10.18653/v1/2021.mrqa-1.15

Association for Computational Linguistics. doi: 10.18653/v1/2021.mrqa-1.15. URL https://aclanthology.org/2021.mrqa-1.15. Timo Schick, Arun Tejasvi Chaganty Dwivedi-Yu, Seyed Iman Hosseini, Peter Sorensen, Teven Le Scao, Christine Akiki, Nouha Tazi, Samia Touileb, Ellie Pavlick...

2021 arXiv
[2023]

doi: 10.18653/v1/2023.acl-long.870

Association for Computational Linguistics. doi: 10.18653/v1/2023.acl-long.870. URL https: //aclanthology.org/2023.acl-long.870. Yunfei Chu, Jin Xu, Qian Yang, Haojie Wei, Xipin Wei, Zhifang Guo, Yichong Leng, Yuanjun Lv, Jinzheng He, Junyang Lin, et al. Qwen2-audio technical r...

2023 arXiv
[2024]

Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, et al

URL https://arxiv.org/abs/2406.09170. Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, et al. The llama 3 herd of models. arXiv preprint arXiv:2407.21783,

arXiv

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [1]

Language models are few-shot learners

Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901,

work page 1901

[2] [5]

Albert Q

URLhttps://arxiv.org/ abs/2110.03215. Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, L´elio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timoth´ee Lacroix, and William El S...

arXiv

[3] [6]

GrowOVER: How Can LLMs Adapt to Growing Real-World Knowledge?

URL https://arxiv.org/abs/2406.05606. 9 Evgenii Kortukov, Alexander Rubinstein, Elisa Nguyen, and Seong Joon Oh. Studying large language model behav- iors under realistic knowledge conflicts,

work page Pith review arXiv

[4] [7]

Quinn Leng, Jacob Portes, Sam Havens, Matei Zaharia, and Michael Carbin

URL https://arxiv.org/abs/2404.16032. Quinn Leng, Jacob Portes, Sam Havens, Matei Zaharia, and Michael Carbin. Long context and rag performance in large language models. https://www.databricks.com/blog/ long-context-rag-performance-llms , August

arXiv

[5] [8]

Mosh Levy, Alon Jacoby, and Yoav Goldberg

Mosaic AI Research, Databricks, Accessed: 2024-09-02. Mosh Levy, Alon Jacoby, and Yoav Goldberg. Same task, more tokens: the impact of input length on the reasoning performance of large language models. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar (eds.), Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1...

work page 2024

[6] [9]

URL https: //aclanthology.org/2024.acl-long.818

Association for Computational Linguistics. URL https: //aclanthology.org/2024.acl-long.818. Belinda Z. Li, Emmy Liu, Alexis Ross, Abbas Zeitoun, Graham Neubig, and Jacob Andreas. Language modeling with editable external knowledge, 2024a. URL https://arxiv.org/abs/2406.11830. Tianle Li, Ge Zhang, Quy Duc Do, Xiang Yue, and Wenhu Chen. Long-context llms str...

arXiv 2024

[7] [10]

URL https://aclanthology.org/2024

doi: 10.1162/tacl a 00638. URL https://aclanthology.org/2024. tacl-1.9. Adam Liˇska, Tom´aˇs Koˇcisk´y, Elena Gribovskaya, Tayfun Terzi, Eren Sezener, Devang Agrawal, Cyprien de Mas- son d’Autume, Tim Scholtes, Manzil Zaheer, Susannah Young, Ellen Gilsenan-McMahon, Sophia Austin, Phil Blunsom, and Angeliki Lazaridou. Streamingqa: A benchmark for adaptatio...

doi:10.1162/tacl 2024

[8] [11]

Kelvin Luu, Daniel Khashabi, Suchin Gururangan, Karishma Mandyam, and Noah A

URL https://arxiv.org/abs/2205.11388. Kelvin Luu, Daniel Khashabi, Suchin Gururangan, Karishma Mandyam, and Noah A. Smith. Time waits for no one! analysis and challenges of temporal misalignment. In Marine Carpuat, Marie-Catherine de Marneffe, and Ivan Vladimir Meza Ruiz (eds.), Proceedings of the 2022 Conference of the North American Chapter of the Assoc...

arXiv 2022