Summary of Multilingual Llms Inherently Reward In-language Time-sensitive Semantic Alignment For Low-resource Languages, by Ashutosh Bajpai et al.
Multilingual LLMs Inherently Reward In-Language Time-Sensitive Semantic Alignment for Low-Resource Languages
by Ashutosh Bajpai, Tanmoy Chakraborty
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper addresses the disparity in labeled resources between high-resource languages and low-resource languages for Large Language Models (LLMs). Recent advances in cross-lingual in-context learning (X-ICL) have shown promise, but this study reveals that LLMs favor in-language semantically aligned cross-lingual instances over direct cross-lingual semantic alignments. The paper proposes a novel method called Cross-Lingual Time-Sensitive Semantic Alignment (CLiTSSA) to improve temporal reasoning capabilities in low-resource languages. This is achieved by introducing the mTEMPREASON dataset, which includes pairs of parallel cross-language temporal queries with anticipated in-language semantic similarity scores. Empirical evidence shows that CLiTSSA outperforms established baselines across three languages and four LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to solve a big problem in language learning. Right now, some languages are much better studied than others because they have more labeled data. This makes it hard for computers to learn new things in those less-studied languages. The researchers found that even when they use a special way of learning called cross-lingual in-context learning, the computers still prefer to use data from their own language rather than trying to understand other languages. They created a new way of doing this, which they call Cross-Lingual Time-Sensitive Semantic Alignment (CLiTSSA). This helps computers better understand time-related things in less-studied languages. The researchers tested CLiTSSA on three different languages and it worked much better than other methods. |
Keywords
» Artificial intelligence » Alignment
