mlsanityData to 7 Oct 2026

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

arXiv 2412.08090 ↗First posted 11 Dec 2024cs.CL cs.AI cs.LGcited by 0

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 16 of its 25 references that are in the map. First posted on arXiv 11 Dec 2024.

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  1. 2023Not All Languages Are Created Equal in LLMs: Improving Multilingual Capability by Cross-Lingual-Thought Prompting11 May

Other references · 15

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  2. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  3. 2021Calibrate Before Use: Improving Few-Shot Performance of Language Modelscited by 1,371
  4. 2021What Makes Good In-Context Examples for GPT-3?cited by 1,184
  5. 2021Time-Aware Language Models as Temporal Knowledge Basescited by 290
  6. 2021A Dataset for Answering Time-Sensitive Questionscited by 150
  7. 2021Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learnerscited by 115
  8. 2021Language Models are Few-shot Multilingual Learnerscited by 92
  9. 2023Multilingual LLMs are Better Cross-lingual In-context Learners with Alignmentcited by 71
  10. 2021Complex Temporal Question Answering on Knowledge Graphscited by 69
  11. 2024LLMs Are Few-Shot In-Context Low-Resource Language Learnerscited by 68
  12. 2024From LLM to NMT: Advancing Low-Resource Machine Translation with Claudecited by 57
3 more
  1. 2023Democratizing LLMs for Low-Resource Languages by Leveraging their English Dominant Abilities with Linguistically-Diverse Promptscited by 45
  2. 2021Time-Stamped Language Model: Teaching Language Models to Understand the Flow of Eventscited by 22
  3. 2024XAMPLER: Learning to Retrieve Cross-Lingual In-Context Examplescited by 5

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  7. 2025Cross-Lingual Consistency: A Novel Inference Framework for Advancing Reasoning in Large Language Models
  8. 2025From Unaligned to Aligned: Scaling Multilingual LLMs with Multi-Way Parallel Corpora