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

• URL: http://arxiv.org/abs/2412.08090v1
• Date: Wed, 11 Dec 2024 04:16:39 GMT
• Title: Multilingual LLMs Inherently Reward In-Language Time-Sensitive Semantic Alignment for Low-Resource Languages
• Authors: Ashutosh Bajpai, Tanmoy Chakraborty,
• Abstract summary: The 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. This study aims to bridge this gap by improving temporal reasoning capabilities in low-resource languages.
• Score: 19.863010475923414
• License:
• 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.

Related papers

• Refining Translations with LLMs: A Constraint-Aware Iterative Prompting Approach [7.5069214839655345]
  Large language models (LLMs) have demonstrated remarkable proficiency in machine translation (MT) We propose a multi-step prompt chain that enhances translation faithfulness by prioritizing key terms crucial for semantic accuracy. Experiments using Llama and Qwen as base models on the FLORES-200 and WMT datasets demonstrate significant improvements over baselines.
  arXiv  Detail & Related papers  (2024-11-13T05:40:24Z)
• Think Carefully and Check Again! Meta-Generation Unlocking LLMs for Low-Resource Cross-Lingual Summarization [108.6908427615402]
  Cross-lingual summarization ( CLS) aims to generate a summary for the source text in a different target language. Currently, instruction-tuned large language models (LLMs) excel at various English tasks. Recent studies have shown that LLMs' performance on CLS tasks remains unsatisfactory even with few-shot settings.
  arXiv  Detail & Related papers  (2024-10-26T00:39:44Z)
• Do Large Language Models Have an English Accent? Evaluating and Improving the Naturalness of Multilingual LLMs [13.558778781305998]
  Large Language Models (LLMs) are predominantly designed with English as the primary language. Even the few that are multilingual tend to exhibit strong English-centric biases. This paper introduces novel automatic corpus-level metrics to assess the lexical and syntactic naturalness of multilingual outputs.
  arXiv  Detail & Related papers  (2024-10-21T12:34:17Z)
• How Do Multilingual Language Models Remember Facts? [50.13632788453612]
  We show that previously identified recall mechanisms in English largely apply to multilingual contexts. We localize the role of language during recall, finding that subject enrichment is language-independent. In decoder-only LLMs, FVs compose these two pieces of information in two separate stages.
  arXiv  Detail & Related papers  (2024-10-18T11:39:34Z)
• Bridging the Language Gaps in Large Language Models with Inference-Time Cross-Lingual Intervention [71.12193680015622]
  Large Language Models (LLMs) have shown remarkable capabilities in natural language processing. LLMs exhibit significant performance gaps among different languages. We propose Inference-Time Cross-Lingual Intervention (INCLINE) to overcome these limitations without incurring significant costs.
  arXiv  Detail & Related papers  (2024-10-16T11:23:03Z)
• Lens: Rethinking Multilingual Enhancement for Large Language Models [70.85065197789639]
  Lens is a novel approach to enhance multilingual capabilities of large language models (LLMs) It operates by manipulating the hidden representations within the language-agnostic and language-specific subspaces from top layers of LLMs. It achieves superior results with much fewer computational resources compared to existing post-training approaches.
  arXiv  Detail & Related papers  (2024-10-06T08:51:30Z)
• A dual task learning approach to fine-tune a multilingual semantic speech encoder for Spoken Language Understanding [12.887586659035497]
  Self-Supervised Learning is vastly used to efficiently represent speech for Spoken Language Understanding. textual SSL models are proposed to encode language-agnostic semantics. SAMU-XLSR framework employed this semantic information to enrich multilingual speech representations.
  arXiv  Detail & Related papers  (2024-06-17T23:07:53Z)
• Cross-Lingual Transfer Robustness to Lower-Resource Languages on Adversarial Datasets [4.653113033432781]
  Cross-lingual transfer capabilities of Multilingual Language Models (MLLMs) are investigated. Our research provides valuable insights into cross-lingual transfer and its implications for NLP applications.
  arXiv  Detail & Related papers  (2024-03-29T08:47:15Z)
• LLMs Are Few-Shot In-Context Low-Resource Language Learners [59.74451570590808]
  In-context learning (ICL) empowers large language models (LLMs) to perform diverse tasks in underrepresented languages. We extensively study ICL and its cross-lingual variation (X-ICL) on 25 low-resource and 7 relatively higher-resource languages. Our study concludes the significance of few-shot in-context information on enhancing the low-resource understanding quality of LLMs.
  arXiv  Detail & Related papers  (2024-03-25T07:55:29Z)
• The Language Barrier: Dissecting Safety Challenges of LLMs in Multilingual Contexts [46.089025223336854]
  This paper examines the variations in safety challenges faced by large language models across different languages. We compare how state-of-the-art LLMs respond to the same set of malicious prompts written in higher- vs. lower-resource languages.
  arXiv  Detail & Related papers  (2024-01-23T23:12:09Z)
• AM2iCo: Evaluating Word Meaning in Context across Low-ResourceLanguages with Adversarial Examples [51.048234591165155]
  We present AM2iCo, Adversarial and Multilingual Meaning in Context. It aims to faithfully assess the ability of state-of-the-art (SotA) representation models to understand the identity of word meaning in cross-lingual contexts. Results reveal that current SotA pretrained encoders substantially lag behind human performance.
  arXiv  Detail & Related papers  (2021-04-17T20:23:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.