Multilingual LLMs Inherently Reward In-Language Time-Sensitive Semantic Alignment for Low-Resource Languages

Ashutosh Bajpai; Tanmoy Chakraborty

REVIEW 4 major objections 4 minor 38 references

Multilingual LLMs Inherently Reward In-Language Time-Sensitive Semantic Alignment for Low-Resource Languages

T0 review · 4 major / 4 minor · reviewed 2026-08-11 · deepseek-v4-flash

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

Pith's one-line read Multilingual LLMs reward in-language similarity scoring over cross-lingual scoring for temporal few-shot retrieval, and CLiTSSA transfers that signal into a retriever that lifts mean F1 by 5.32, 1.62, and 1.43 points in French, German…

desk verdict A useful multilingual temporal-reasoning dataset and a plausible cross-lingual retriever, undercut by an overstated 'inherently rewards' claim and thin validation of the retrieval mechanism. read the letter →

arxiv 2412.08090 v2 pith:X6JHPUUD submitted 2024-12-11 cs.CL cs.AIcs.LG

Ashutosh Bajpai , Tanmoy Chakraborty This is my paper · ORCID

classification cs.CLcs.AIcs.LG

keywords temporalreasoningcross-lingualin-contextlearninglow-resourcelanguagessemanticalignmentretrieval-augmentedpromptingmultilingualLLMsmTEMPREASONCLiTSSA

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

Temporal reasoning questions posed in a low-resource language are best answered when the English few-shot examples shown to the LLM are chosen by first translating those examples into the query's language, scoring semantic similarity there, and only then retrieving. The authors demonstrate that this in-language scoring outperforms direct cross-lingual similarity in multilingual embedding space, and they turn that observation into a method: CLiTSSA fine-tunes a multilingual Sentence-BERT retriever with CoSENT loss to reproduce in-language similarity scores from cross-lingual query–example pairs. They also contribute mTEMPREASON, a French/German/Romanian translation of the TEMPREASON temporal benchmark with three levels of temporal tasks. In three-shot settings across four LLMs, CLiTSSA improves mean F1 over the X-InSTA semantic-alignment baseline by 5.32, 1.62, and 1.43 points for French, German, and Romanian, with the largest gains on the simplest time–time task. If the finding holds, retrieval for low-resource in-context learning should be trained to imitate monolingual similarity rather than rely on the encoder's raw cross-lingual similarity.

What carries the argument

The central object is the in-language similarity score, f(s)_u,v, computed by a multilingual Sentence-BERT encoder between a low-resource query ql_u and a T5-translated English query qr'_v in the low-resource language's monolingual embedding space. CLiTSSA uses these scores as supervised labels to fine-tune the same encoder (with CoSENT loss) on cross-lingual pairs (ql_u, qr_v), so the retriever learns to rank English examples by how semantically close they are to the query when both are read in the query's language. The CoSENT loss is the training mechanism that distills the monolingual ranking into the cross-lingual space.

What would settle it

Retrieve examples for a held-out set of low-resource temporal queries using three scorers: in-language similarity, direct cross-lingual similarity, and an oracle score equal to the downstream F1 gain each English example actually produces for the LLM. If a retriever trained on the oracle or on direct cross-lingual scores matches or beats CLiTSSA, or if the rank correlation between in-language similarity and oracle usefulness is near zero, the paper's central claim would fail.

Watch

Extended reading notes

Core claim

The central discovery is that multilingual LLMs intrinsically reward in-language semantic alignment for time-sensitive cross-lingual in-context learning: for the L1 time–time task, retrieving the top-3 English examples by in-language similarity (query vs. translated English) gives F1 of 43.48, 64.77, and 60.50 for Romanian, German, and French, versus 33.65, 56.63, and 46.62 for direct cross-lingual similarity. CLiTSSA operationalizes this by constructing a training set of (low-resource query, English example) pairs labeled with the cosine similarity between the query and the T5-translated English example in the multilingual Sentence-BERT space, subsampling top-h and random-w pairs per query, and fine-tuning the retriever with CoSENT loss so that cross-lingual embeddings reproduce those in-language scores. The fine-tuned retriever then selects English demonstrations that improve LLaMA3-8B's temporal QA across three tasks and three languages, and the gains transfer across four LLMs, with the largest relative improvements on the French L1 task.

Load-bearing premise

The load-bearing premise is that the semantic similarity scores a multilingual sentence-encoder computes in the low-resource language—between a query and a translated English example—genuinely track which English demonstrations will improve the LLM's answer.

Editorial extensions

If this is right

Cross-lingual retrievers for low-resource QA should be trained with in-language similarity labels, not direct cross-lingual cosine similarity, when the goal is downstream LLM accuracy.
Translation is a legitimate and useful preprocessing step for building retrieval training data, even for languages the LLM can already process.
Temporal reasoning in low-resource languages can be improved without modifying the LLM itself, purely by changing which English demonstrations are placed in the prompt.
Fine-tuning the retriever on the simplest time–time task (L1) transfers to harder time–event and event–event tasks, so the alignment signal is partly task-agnostic within temporal reasoning.
The mTEMPREASON dataset provides a reusable benchmark for future work on low-resource temporal reasoning across French, German, and Romanian.

Reading between the lines

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

A likely consequence not tested in the paper: the in-language advantage may reflect a weakness of the base multilingual encoder's cross-lingual alignment for temporal vocabulary rather than a property of LLM inference itself; using a stronger cross-lingual encoder as the teacher could shrink the gap or eliminate it.
Because the training labels come from T5 translations, the method inherits translation noise; in lower-resource languages without reliable machine translation, the same recipe would need pivot languages or seed parallel data, which the paper does not address.
The transfer idea is not obviously specific to temporality; a parallel experiment on arithmetic or commonsense QA could reveal whether in-language scoring helps all low-resource ICL or only queries with time-sensitive semantics.
The paper's cross-task result suggests that a single retriever fine-tuned on the easiest temporal task could serve as a general semantic aligner for a low-resource language, a possibility the authors raise but do not fully explore.

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. First, this paper gives the community something real: mTEMPREASON, a translated version of TEMPREASON for Romanian, German, and French, with quality checks, and CLiTSSA, a retriever that distills in-language similarity scores into a cross-lingual space for temporal queries. The gains over X-InSTA are consistent across three languages, three tasks, and four LLMs—not huge, but steady. Second, the headline claim that multilingual LLMs 'inherently reward' in-language alignment is wider than the evidence. Table 1, the only direct support, covers one task (L1) and one model (LLaMA3-8B). That is a meaningful observation, but it does not support 'inherently.'

What the paper does well: the dataset construction is transparent, with TSR and back-translation BLEU-3 reported, and the code/data are public. The method is simple to understand and reproduce: use multilingual SBERT to score low-resource queries against translated English examples, then fine-tune the same model with CoSENT to make cross-lingual pairs produce those scores. The cross-task and cross-LLM experiments are a useful addition, and the few-shot and h/w ablations are honest.

The soft spots are real but not fatal. Statistical support is thin: a single Mann-Whitney U with p=0.05, no confidence intervals, and three runs that only vary top_p. The baseline set is too narrow: XAMPLER, a learned cross-lingual retriever, is cited but not included, and there is no BM25 or lexical baseline. So the gains over X-InSTA could come from any learned retriever, not necessarily the specific in-language distillation idea. The stress-test is partly right. The labels are produced by the same base model, so the appendix histogram is unsurprising. But note the fine-tuning inputs are cross-lingual pairs (low-resource query + English example) while the labels come from the monolingual space (low-resource query + translated English). That is a genuine transfer task, not pure self-distillation. The missing piece is whether those scores track usefulness for the LLM; the paper doesn't show that, and the downstream gains, while consistent, don't isolate the mechanism.

The paper itself flags some of these limits in the error analysis and the conclusion's future work, which I read as honest. The dataset alone is a usable contribution, and the method is reasonable, but the 'inherently' claim should be tempered to 'this method works in these setups' until the label-quality question is answered.

Who is this for? Researchers working on cross-lingual ICL, multilingual QA, or temporal reasoning will find it useful, and the dataset is worth citing. It deserves a serious referee—the issues are fixable with more baselines, per-run variance reporting, and a more careful claim. I'd recommend sending it to review, with the expectation of major revision.

Referee Report

4 major / 4 minor

Summary. The paper argues that multilingual LLMs 'inherently reward' in-language semantically aligned cross-lingual demonstrations over direct cross-lingual semantic alignment for time-sensitive queries, based on a comparison of two retrieval scoring procedures (Table 1). It introduces mTEMPREASON, a T5-translated version of TEMPREASON in Romanian, German, and French, and proposes CLiTSSA, a retriever fine-tuned from multilingual Sentence-BERT with CoSENT loss. The training pairs for CLiTSSA are cross-lingual query pairs labeled by cosine similarity in the low-resource monolingual embedding space. The paper reports F1 and EM improvements over X-ICL and X-InSTA across three languages, three temporal tasks, and four LLMs, with mean F1 gains of 5.32, 1.62, and 1.43 for French, German, and Romanian, respectively, and appendix analyses of embedding-space shifts, cross-task transfer, and monolingual-vs-cross-lingual gaps.

Significance. If the central claims are supported, the paper would make a useful contribution: mTEMPREASON addresses a genuine gap in multilingual temporal reasoning evaluation, and the proposed retrieval training signal is simple and potentially transferable. The paper also ships code and data, and the experimental scope is respectable, spanning multiple languages, tasks, and models. However, the strongest claim in the title and abstract—that multilingual LLMs inherently reward in-language semantic alignment—is currently supported by a very narrow experiment, and the mechanism behind CLiTSSA's gains lacks independent validation. The paper's value would be substantially increased by strengthening these two points.

major comments (4)

[Table 1 and 'Our Proposed Method'] The central generalization that multilingual LLMs 'inherently reward' in-language semantic alignment over cross-lingual alignment rests on a single experiment: Table 1 reports only the L1 (time–time) task, one LLM (LLaMA3-8B), top-3 retrieval, and no significance testing. This is too narrow to support the title's universal claim. The authors should either extend Table 1 to L2/L3 tasks and additional LLMs (or at least report the same comparison for the other models in Table 5), or substantially qualify the claim as specific to the tested setting.
[Method, Eq. (3)-(4), and Appendix Figures 5-7] The training signal for CLiTSSA is generated by the same multilingual Sentence-BERT model that is then fine-tuned: the labels f(s)_u,v in Eq. (3) come from distiluse-base-multilingual-cased-v1, and the same model is optimized with CoSENT loss in Eq. (4). The appendix histograms show that after fine-tuning, positive pairs (English query-translation pairs) move closer and negative pairs move apart, but this is largely a check that the model learned its own training objective. The paper provides no external validation that the pre-fine-tuning in-language similarity scores track whether a demonstration actually improves downstream temporal QA. Without such validation—for example, an oracle-based retrieval-quality evaluation, a correlation analysis between label score and downstream gain, or a non-semantic baseline such as BM25 over translated examples—the modest F1 gains in Tables 4 and 5 are not tied to the proposed mechanism; they could arise from lexical overlap or self-distillation artifacts.
[Table 4 statistics] The statistical support for the main comparison is thin. The paper reports a one-tailed Mann-Whitney U p-value of 0.05 for comparing mean F1 of CLiTSSA with X-InSTA 'across languages and tasks,' but this does not establish that each of the per-cell gains in Table 4 is significant, and no correction is applied for the multiple comparisons implicit in claiming gains for each language and task. The authors should report per-condition significance tests with a clearly described null distribution (e.g., across the three top_p runs, or across bootstrapped samples) and correct for multiple testing, or explicitly state which comparisons are not individually significant.
[Table 5, 'Robustness Across LLMs'] The robustness claim across LLMs is not backed by significance testing. The mean F1 improvement over X-InSTA is 9.68 for L1, 2.33 for L2, and 0.58 for L3; the L3 improvement is within the range of run-to-run noise one would expect from varying top_p among {1.0, 0.8, 0.6}. Without per-model per-task tests or confidence intervals, the conclusion that CLiTSSA is robust across LLMs is stronger than the evidence.

minor comments (4)

[Eq. (4)] Equation (4) contains an unfinished placeholder ('exp ...') and is not a complete mathematical expression; since this is the central training loss, the complete CoSENT loss should be written out.
[Appendix, 'Hyperparameters'] The sentence 'This study used the This study used the Huggingface repository...' contains a duplicated phrase and should be edited.
[Table 11] In Table 11, the Zero-Shot row for L1 appears to have misaligned numbers under the F1/EM columns, and the Δ rows are difficult to interpret; the table should be reformatted with clear column separators and explicit labels for the reported deltas.
[Method, sentence after Eq. (2)] The phrase 'the procedure stats with the extraction' should read 'starts with the extraction.'

Circularity Check

1 steps flagged · score 2.0 of 10

Minor self-referential validation in the appendix; the core F1 evaluation is external and not circular.

self definitional [Technical Appendix, 'Evolution Of Embedding Space Under CLiTSSA' (Figures 5-7)]
"The empirical outcomes, as depicted in Figures referenced as Figure 5 (for Romanian), Figure 6 (for German), and Figure 7 (for French), across varied temporal tasks, unequivocally demonstrate the augmented capacity of CLiTSSA, in heightening the semantic congruence for positively aligned query pairs while concurrently diminishing the semantic connection for antagonistic pairs. This enhancement in the model’s embedding space unequivocally substantiates its improved performance in terms of F1 scores across the temporal tasks and low-resource languages."

The pairs used in this appendix are defined by the same embedding geometry the retriever is trained to alter: positive pairs are English queries paired with their low-resource translations, and antagonistic pairs are sampled as pairs whose pre-fine-tuning cosine similarity is at most 0.5. The CoSENT objective (Eq. 4) is exactly to raise similarity for high-label pairs and lower it for low-label pairs. Showing that post-fine-tuning cosine similarity increases for positive pairs and decreases for antagonistic pairs therefore demonstrates that the model optimized its own training objective; it is not an independent confirmation that the retrieved English examples improve LLM temporal QA.

full rationale

The core derivation is not circular. Table 1 is an empirical comparison of two retrieval strategies (cross-lingual similarity vs. in-language similarity) evaluated by downstream LLaMA3-8B F1/EM; the result that in-language similarity retrieves better demonstrations is an external measurement, not a definitional consequence. CLiTSSA's training data (Eq. 3) uses cosine similarity labels from distiluse-base-multilingual-cased-v1 computed in the low-resource monolingual space, and the same base model is then fine-tuned with CoSENT loss to reproduce those labels in a cross-lingual space. This is self-distillation and therefore inherits any biases of the base model's similarity scores, a real limitation that the paper does not directly validate (e.g., no oracle or non-semantic retrieval baseline such as BM25 on translations). The paper itself acknowledges the dependence in its Error Analysis, where it states that inaccuracies may propagate from the monolingual to the cross-lingual space. However, the central evaluation is held-out LLM temporal QA performance on the mTEMPREASON test set against X-ICL and X-InSTA baselines. Those F1 gains are external to the training labels and could have failed, so they are not forced by construction. The paper's self-citations (Tanwar et al. 2023 as the X-InSTA baseline; Bajpai et al. 2024 in related work) are not load-bearing: X-InSTA is used as a competing method, not as a justification of the key premise, and no uniqueness theorem is imported from the authors' prior work. The only genuinely self-referential piece is the appendix's embedding-space histogram analysis, which verifies that the model learned its own CoSENT objective and is then used to 'substantiate' the F1 improvement; this is a circular validation step, but it is supplementary rather than the basis of the main empirical claim. Overall circularity is therefore minor.

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

The central evaluation depends on three hyperparameters chosen by ablation or heuristics (h, w, k, top_p) and on two domain assumptions: that SBERT monolingual similarity is a useful label source for cross-lingual retrieval, and that T5 translations preserve the temporal content. No new physical or conceptual entities are postulated.

free parameters (4)

h (top analogous examples per query) = 30
Chosen by iterative filtering on KL divergence between subsample and full similarity distribution; not tied to downstream performance.
w (random negative examples per query) = 10
Chosen alongside h via the same heuristic procedure to balance distribution coverage and training size.
k (number of in-context examples) = 3
Selected after a 1-shot, 2-shot, 3-shot ablation on French showing saturation at 3.
top_p values for decoding = 1.0, 0.8, 0.6
Used to produce three runs per condition; results are means over these values. This is a decoding parameter, not a fitted scientific constant, but it affects the reported scores.

assumptions (3)

domain assumption Multilingual Sentence-BERT embeddings in a monolingual, translated space are a valid proxy for semantic usefulness of retrieval examples.
The entire Dt construction in the Method section uses f(s)_u,v from distiluse-base-multilingual-cased-v1 as labels without external validation against downstream task performance.
domain assumption T5-generated translations of the TEMPREASON dataset preserve the temporal semantics needed for QA.
Translation quality is checked via TSR and back-translation BLEU-3 on 100 samples per language, but not by human evaluation of answer preservation; entity names and dates are assumed to survive translation.
domain assumption CoSENT loss, combined with pairwise similarity labels, shapes the cross-lingual embedding space in a way that transfers to unseen queries.
The paper relies on this standard loss and does not compare with alternative fine-tuning objectives.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Multilingual LLMs Inherently Reward In-Language Time-Sensitive Semantic Alignment for Low-Resource Languages." pith.science (2026). https://pith.science/paper/X6JHPUUD

@misc{pith2026241208090,
  author       = {Pith},
  title        = {Pith review of: Multilingual LLMs Inherently Reward In-Language Time-Sensitive Semantic Alignment for Low-Resource Languages},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/X6JHPUUD}},
  note         = {Machine review of arXiv:2412.08090}
}

read the original abstract

The unwavering disparity in labeled resources between resource-rich languages and those considered low-resource remains a significant impediment for Large Language Models (LLMs). Recent strides in cross-lingual in-context learning (X-ICL), mainly through semantically aligned examples retrieved from multilingual pre-trained transformers, have shown promise in mitigating this issue. However, our investigation reveals that LLMs intrinsically reward in-language semantically aligned cross-lingual instances over direct cross-lingual semantic alignments, with a pronounced disparity in handling time-sensitive queries in the X-ICL setup. Such queries demand sound temporal reasoning ability from LLMs, yet the advancements have predominantly focused on English. This study aims to bridge this gap by improving temporal reasoning capabilities in low-resource languages. To this end, we introduce mTEMPREASON, a temporal reasoning dataset aimed at the varied degrees of low-resource languages and propose Cross-Lingual Time-Sensitive Semantic Alignment (CLiTSSA), a novel method to improve temporal reasoning in these contexts. To facilitate this, we construct an extension of mTEMPREASON comprising pairs of parallel cross-language temporal queries along with their anticipated in-language semantic similarity scores. Our empirical evidence underscores the superior performance of CLiTSSA compared to established baselines across three languages -- Romanian, German, and French, encompassing three temporal tasks and including a diverse set of four contemporaneous LLMs. This marks a significant step forward in addressing resource disparity in the context of temporal reasoning across languages.

Figures

Figures reproduced from arXiv: 2412.08090 by the authors.

**Figure 3.** Cross–Task CLiTSSA performance across tasks with F1 scores on the French test set against the X-InSTA baseline. CLiTSSA-L∗ represents a retriever fine-tuned using L* training dataset where ∗ ∈ {1, 2, 3}. quired through the lower-level temporal task (L1) can significantly enhance the relative F1-score of the more complex tasks L2 and L3 by 13.5% and 14.0%, respectively. However, the reverse scenario is inapplicabl… view at source ↗

**Figure 4.** A comparative analysis of F1 scores across tempoLoading [MathJax]/extensions/MathMenu.js [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗

Figures from the paper (2 more)

**Figure 5.** Figure 5: Histogram-based comparison of embedding space [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]

**Figure 6.** Figure 6: Histogram-based comparison of embedding space [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

38 extracted references · 15 canonical work pages

[1]

, " * write output.state after.block = add.period write newline

ENTRY address archivePrefix author booktitle chapter edition editor eid eprint howpublished institution isbn journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.a...
[2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...
[3]

F.; Cahyawijaya, S.; Aji, A

Adilazuarda, M. F.; Cahyawijaya, S.; Aji, A. F.; Winata, G. I.; and Purwarianti, A. 2024. LinguAlchemy: Fusing Typological and Geographical Elements for Unseen Language Generalization. arXiv:2401.06034

arXiv 2024
[4]

AI@Meta. 2024. Llama 3 Model Card

2024
[5]

V.; Blevins, T.; Gonen, H.; Reid, M.; Tsvetkov, Y.; Ruder, S.; and Hajishirzi, H

Asai, A.; Kudugunta, S.; Yu, X. V.; Blevins, T.; Gonen, H.; Reid, M.; Tsvetkov, Y.; Ruder, S.; and Hajishirzi, H. 2023. BUFFET: Benchmarking Large Language Models for Few-shot Cross-lingual Transfer. arXiv:2305.14857

arXiv 2023
[6]

F.; Winata, G

Cahyawijaya, S.; Lovenia, H.; Aji, A. F.; Winata, G. I.; Wilie, B.; Mahendra, R.; Wibisono, C.; Romadhony, A.; Vincentio, K.; Koto, F.; Santoso, J.; Moeljadi, D.; Wirawan, C.; Hudi, F.; Parmonangan, I. H.; Alfina, I.; Wicaksono, M. S.; Putra, I. F.; Rahmadani, S.; Oenang, Y.; Septiandri, A. A.; Jaya, J.; Dhole, K. D.; Suryani, A. A.; Putri, R. A.; Su, D.;...

arXiv 2023
[7]

Cahyawijaya, S.; Lovenia, H.; and Fung, P. 2024. LLMs Are Few-Shot In-Context Low-Resource Language Learners. arXiv:2403.16512

arXiv 2024
[8]

Chen, W.; Wang, X.; and Wang, W. Y. 2021. A Dataset for Answering Time-Sensitive Questions. arXiv:2108.06314

arXiv 2021

Show all 38 references

[9]

R.; Eisenschlos, J

Dhingra, B.; Cole, J. R.; Eisenschlos, J. M.; Gillick, D.; Eisenstein, J.; and Cohen, W. W. 2022. Time-Aware Language Models as Temporal Knowledge Bases. Transactions of the Association for Computational Linguistics, 10: 257--273

2022
[10]

Enis, M.; and Hopkins, M. 2024. From LLM to NMT: Advancing Low-Resource Machine Translation with Claude. arXiv:2404.13813

2024 arXiv
[11]

X.; Song, T.; Xia, Y.; and Wei, F

Huang, H.; Tang, T.; Zhang, D.; Zhao, W. X.; Song, T.; Xia, Y.; and Wei, F. 2023. Not All Languages Are Created Equal in LLMs: Improving Multilingual Capability by Cross-Lingual-Thought Prompting. arXiv:2305.07004

2023 arXiv
[12]

Jia, Z.; Abujabal, A.; Saha Roy, R.; Strötgen, J.; and Weikum, G. 2018. TEQUILA: Temporal Question Answering over Knowledge Bases. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM ’18. ACM

2018
[13]

Jia, Z.; Pramanik, S.; Saha Roy, R.; and Weikum, G. 2021. Complex Temporal Question Answering on Knowledge Graphs. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management, CIKM ’21. ACM

2021
[14]

Q.; Sablayrolles, A.; Mensch, A.; Bamford, C.; Chaplot, D

Jiang, A. Q.; Sablayrolles, A.; Mensch, A.; Bamford, C.; Chaplot, D. S.; de las Casas, D.; Bressand, F.; Lengyel, G.; Lample, G.; Saulnier, L.; Lavaud, L. R.; Lachaux, M.-A.; Stock, P.; Scao, T. L.; Lavril, T.; Wang, T.; Lacroix, T.; and Sayed, W. E. 2023. Mistral 7B. arXiv:2310.06825

2023 arXiv
[15]

Lin, P.; Martins, A. F. T.; and Schütze, H. 2024. XAMPLER: Learning to Retrieve Cross-Lingual In-Context Examples. arXiv:2405.05116

2024 arXiv
[16]

V.; Mihaylov, T.; Artetxe, M.; Wang, T.; Chen, S.; Simig, D.; Ott, M.; Goyal, N.; Bhosale, S.; Du, J.; Pasunuru, R.; Shleifer, S.; Koura, P

Lin, X. V.; Mihaylov, T.; Artetxe, M.; Wang, T.; Chen, S.; Simig, D.; Ott, M.; Goyal, N.; Bhosale, S.; Du, J.; Pasunuru, R.; Shleifer, S.; Koura, P. S.; Chaudhary, V.; O ' Horo, B.; Wang, J.; Zettlemoyer, L.; Kozareva, Z.; Diab, M.; Stoyanov, V.; and Li, X. 2022 a . Few-shot L...

2022
[17]

V.; Mihaylov, T.; Artetxe, M.; Wang, T.; Chen, S.; Simig, D.; Ott, M.; Goyal, N.; Bhosale, S.; Du, J.; Pasunuru, R.; Shleifer, S.; Koura, P

Lin, X. V.; Mihaylov, T.; Artetxe, M.; Wang, T.; Chen, S.; Simig, D.; Ott, M.; Goyal, N.; Bhosale, S.; Du, J.; Pasunuru, R.; Shleifer, S.; Koura, P. S.; Chaudhary, V.; O'Horo, B.; Wang, J.; Zettlemoyer, L.; Kozareva, Z.; Diab, M.; Stoyanov, V.; and Li, X. 2022 b . Few-shot Lea...

2022 arXiv
[18]

Liu, J.; Shen, D.; Zhang, Y.; Dolan, B.; Carin, L.; and Chen, W. 2022. What Makes Good In-Context Examples for GPT -3? In Agirre, E.; Apidianaki, M.; and Vuli \'c , I., eds., Proceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd Workshop on Knowledge Extraction and In...

2022
[19]

Miyabe, M.; and Yoshino, T. 2015. Evaluation of the Validity of Back-Translation as a Method of Assessing the Accuracy of Machine Translation. 2015 International Conference on Culture and Computing (Culture Computing), 145--150

2015
[20]

L.; Bari, M

Muennighoff, N.; Wang, T.; Sutawika, L.; Roberts, A.; Biderman, S.; Scao, T. L.; Bari, M. S.; Shen, S.; Yong, Z.-X.; Schoelkopf, H.; Tang, X.; Radev, D.; Aji, A. F.; Almubarak, K.; Albanie, S.; Alyafeai, Z.; Webson, A.; Raff, E.; and Raffel, C. 2023. Crosslingual Generalizatio...

2023 arXiv
[21]

M.; Joty, S.; and Bing, L

Nguyen, X.-P.; Aljunied, S. M.; Joty, S.; and Bing, L. 2024. Democratizing LLMs for Low-Resource Languages by Leveraging their English Dominant Abilities with Linguistically-Diverse Prompts. arXiv:2306.11372

2024 arXiv
[22]

Pustejovsky, J.; Hanks, P.; Saurí, R.; See, A.; Gaizauskas, R.; Setzer, A.; Radev, D.; Sundheim, B.; Day, D.; Ferro, L.; and Lazo, M. 2003. The TimeBank corpus. Proceedings of Corpus Linguistics

2003
[23]

Qin, L.; Chen, Q.; Zhou, Y.; Chen, Z.; Li, Y.; Liao, L.; Li, M.; Che, W.; and Yu, P. S. 2024. Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontiers. arXiv:2404.04925

2024 arXiv
[24]

Radford, A.; Wu, J.; Child, R.; Luan, D.; Amodei, D.; and Sutskever, I. 2019. Language Models are Unsupervised Multitask Learners

2019
[25]

Raffel, C.; Shazeer, N.; Roberts, A.; Lee, K.; Narang, S.; Matena, M.; Zhou, Y.; Li, W.; and Liu, P. J. 2023. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. arXiv:1910.10683

2023 arXiv
[26]

Rajaby Faghihi, H.; and Kordjamshidi, P. 2021. Time-Stamped Language Model: Teaching Language Models to Understand The Flow of Events. In Toutanova, K.; Rumshisky, A.; Zettlemoyer, L.; Hakkani-Tur, D.; Beltagy, I.; Bethard, S.; Cotterell, R.; Chakraborty, T.; and Zhou, Y., eds...

2021
[27]

Reimers, N.; and Gurevych, I. 2019. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. arXiv:1908.10084

2019 arXiv
[28]

Rubin, O.; Herzig, J.; and Berant, J. 2022. Learning To Retrieve Prompts for In-Context Learning. In Carpuat, M.; de Marneffe, M.-C.; and Meza Ruiz, I. V., eds., Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: ...

2022
[29]

Saxena, A.; Chakrabarti, S.; and Talukdar, P. 2021. Question Answering Over Temporal Knowledge Graphs. In Zong, C.; Xia, F.; Li, W.; and Navigli, R., eds., Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint ...

2021
[30]

T.; and Bing, L

Tan, Q.; Ng, H. T.; and Bing, L. 2023. Towards Benchmarking and Improving the Temporal Reasoning Capability of Large Language Models. arXiv:2306.08952

2023 arXiv
[31]

Tanwar, E.; Dutta, S.; Borthakur, M.; and Chakraborty, T. 2023. Multilingual LLMs are Better Cross-lingual In-context Learners with Alignment. arXiv:2305.05940

2023 arXiv
[32]

Verhagen, M.; Saur \' , R.; Caselli, T.; and Pustejovsky, J. 2010. S em E val-2010 Task 13: T emp E val-2. In Erk, K.; and Strapparava, C., eds., Proceedings of the 5th International Workshop on Semantic Evaluation, 57--62. Uppsala, Sweden: Association for Computational Linguistics

2010
[33]

Wenzek, G.; Lachaux, M.-A.; Conneau, A.; Chaudhary, V.; Guzm \'a n, F.; Joulin, A.; and Grave, E. 2020. CCN et: Extracting High Quality Monolingual Datasets from Web Crawl Data. In Calzolari, N.; B \'e chet, F.; Blache, P.; Choukri, K.; Cieri, C.; Declerck, T.; Goggi, S.; Isah...

2020
[34]

I.; Madotto, A.; Lin, Z.; Liu, R.; Yosinski, J.; and Fung, P

Winata, G. I.; Madotto, A.; Lin, Z.; Liu, R.; Yosinski, J.; and Fung, P. 2021. Language Models are Few-shot Multilingual Learners. In Ataman, D.; Birch, A.; Conneau, A.; Firat, O.; Ruder, S.; and Sahin, G. G., eds., Proceedings of the 1st Workshop on Multilingual Representatio...

2021
[35]

Yamada, I.; and Ri, R. 2024. LEIA: Facilitating Cross-lingual Knowledge Transfer in Language Models with Entity-based Data Augmentation. arXiv:2402.11485

2024 arXiv
[36]

Zhang, N.; Li, L.; Chen, X.; Deng, S.; Bi, Z.; Tan, C.; Huang, F.; and Chen, H. 2022. Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners. arXiv:2108.13161

2022 arXiv
[37]

Z.; Wallace, E.; Feng, S.; Klein, D.; and Singh, S

Zhao, T. Z.; Wallace, E.; Feng, S.; Klein, D.; and Singh, S. 2021. Calibrate Before Use: Improving Few-Shot Performance of Language Models. arXiv:2102.09690

2021 arXiv
[38]

P.; Zhang, H.; Gonzalez, J

Zheng, L.; Chiang, W.-L.; Sheng, Y.; Zhuang, S.; Wu, Z.; Zhuang, Y.; Lin, Z.; Li, Z.; Li, D.; Xing, E. P.; Zhang, H.; Gonzalez, J. E.; and Stoica, I. 2023. Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena. arXiv:2306.05685

2023 arXiv

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.

[1] [1]

, " * write output.state after.block = add.period write newline

ENTRY address archivePrefix author booktitle chapter edition editor eid eprint howpublished institution isbn journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.a...

[2] [2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

[3] [3]

F.; Cahyawijaya, S.; Aji, A

Adilazuarda, M. F.; Cahyawijaya, S.; Aji, A. F.; Winata, G. I.; and Purwarianti, A. 2024. LinguAlchemy: Fusing Typological and Geographical Elements for Unseen Language Generalization. arXiv:2401.06034

arXiv 2024

[4] [4]

AI@Meta. 2024. Llama 3 Model Card

2024

[5] [5]

V.; Blevins, T.; Gonen, H.; Reid, M.; Tsvetkov, Y.; Ruder, S.; and Hajishirzi, H

Asai, A.; Kudugunta, S.; Yu, X. V.; Blevins, T.; Gonen, H.; Reid, M.; Tsvetkov, Y.; Ruder, S.; and Hajishirzi, H. 2023. BUFFET: Benchmarking Large Language Models for Few-shot Cross-lingual Transfer. arXiv:2305.14857

arXiv 2023

[6] [6]

F.; Winata, G

Cahyawijaya, S.; Lovenia, H.; Aji, A. F.; Winata, G. I.; Wilie, B.; Mahendra, R.; Wibisono, C.; Romadhony, A.; Vincentio, K.; Koto, F.; Santoso, J.; Moeljadi, D.; Wirawan, C.; Hudi, F.; Parmonangan, I. H.; Alfina, I.; Wicaksono, M. S.; Putra, I. F.; Rahmadani, S.; Oenang, Y.; Septiandri, A. A.; Jaya, J.; Dhole, K. D.; Suryani, A. A.; Putri, R. A.; Su, D.;...

arXiv 2023

[7] [7]

Cahyawijaya, S.; Lovenia, H.; and Fung, P. 2024. LLMs Are Few-Shot In-Context Low-Resource Language Learners. arXiv:2403.16512

arXiv 2024

[8] [8]

Chen, W.; Wang, X.; and Wang, W. Y. 2021. A Dataset for Answering Time-Sensitive Questions. arXiv:2108.06314

arXiv 2021