Code-mixed LLM: Improve Large Language Models' Capability to Handle Code-Mixing through Reinforcement Learning from AI Feedback
- URL: http://arxiv.org/abs/2411.09073v1
- Date: Wed, 13 Nov 2024 22:56:00 GMT
- Title: Code-mixed LLM: Improve Large Language Models' Capability to Handle Code-Mixing through Reinforcement Learning from AI Feedback
- Authors: Wenbo Zhang, Aditya Majumdar, Amulya Yadav,
- Abstract summary: Code-mixing introduces unique challenges in daily life, such as syntactic mismatches and semantic blending.
Large language models (LLMs) have revolutionized the field of natural language processing (NLP) by offering unprecedented capabilities in understanding human languages.
We propose to improve the multilingual LLMs' ability to understand code-mixing through reinforcement learning from human feedback (RLHF) and code-mixed machine translation tasks.
- Score: 11.223762031003671
- License:
- Abstract: Code-mixing(CM) or code-switching(CSW) refers to the juxtaposition of linguistic units from two or more languages during the conversation or sometimes even a single utterance. Code-mixing introduces unique challenges in daily life, such as syntactic mismatches and semantic blending, that are rarely encountered in monolingual settings. Large language models (LLMs) have revolutionized the field of natural language processing (NLP) by offering unprecedented capabilities in understanding human languages. However, the effectiveness of current state-of-the-art multilingual LLMs has not yet been fully explored in the CM scenario. To fill this gap, we first benchmark the performance of multilingual LLMs on various code-mixing NLP tasks. Then we propose to improve the multilingual LLMs' ability to understand code-mixing through reinforcement learning from human feedback (RLHF) and code-mixed machine translation tasks. Given the high-cost and time-consuming preference labeling procedure, we improve this by utilizing LLMs as annotators to perform the reinforcement learning from AI feedback (RLAIF). The experiments show the effectiveness of the proposed method.
Related papers
- Crystal: Illuminating LLM Abilities on Language and Code [58.5467653736537]
We propose a pretraining strategy to enhance the integration of natural language and coding capabilities.
The resulting model, Crystal, demonstrates remarkable capabilities in both domains.
arXiv Detail & Related papers (2024-11-06T10:28:46Z) - Enhancing Multilingual Speech Generation and Recognition Abilities in LLMs with Constructed Code-switched Data [30.966072545451183]
We propose a MutltiLingual MultiTask (MLMT) model, integrating multilingual speech generation and recognition tasks within the single LLM.
We develop an effective data construction approach that splits and equips words from different languages to equip synthesiss with CS ability without relying on CS data.
arXiv Detail & Related papers (2024-09-17T08:11:07Z) - Bridging the Language Gap: Enhancing Multilingual Prompt-Based Code Generation in LLMs via Zero-Shot Cross-Lingual Transfer [5.355430735475281]
This paper investigates the complexities of multilingual prompt-based code generation.
Our evaluations reveal significant disparities in code quality for non-English prompts.
We propose a zero-shot cross-lingual approach using a neural projection technique.
arXiv Detail & Related papers (2024-08-19T05:11:46Z) - Getting More from Less: Large Language Models are Good Spontaneous Multilingual Learners [67.85635044939836]
Large Language Models (LLMs) have shown impressive language capabilities.
In this work, we investigate the spontaneous multilingual alignment improvement of LLMs.
We find that LLMs instruction-tuned on the question translation data (i.e. without annotated answers) are able to encourage the alignment between English and a wide range of languages.
arXiv Detail & Related papers (2024-05-22T16:46:19Z) - Teaching a Multilingual Large Language Model to Understand Multilingual Speech via Multi-Instructional Training [29.47243668154796]
BLOOMZMMS is a novel model that integrates a multilingual LLM with a multilingual speech encoder.
We demonstrate the transferability of linguistic knowledge from the text to the speech modality.
Our zero-shot evaluation results confirm the robustness of our approach across multiple tasks.
arXiv Detail & Related papers (2024-04-16T21:45:59Z) - Is Translation All You Need? A Study on Solving Multilingual Tasks with Large Language Models [79.46179534911019]
Large language models (LLMs) have demonstrated multilingual capabilities; yet, they are mostly English-centric due to imbalanced training corpora.
This work extends the evaluation from NLP tasks to real user queries.
For culture-related tasks that need deep language understanding, prompting in the native language tends to be more promising.
arXiv Detail & Related papers (2024-03-15T12:47:39Z) - Teaching Machines to Code: Smart Contract Translation with LLMs [4.780973517287942]
We present a pioneering approach, which harnesses the synergy of two distinct large language models (LLMs) within a unified framework.
This framework is designed to grasp coding principles and apply this understanding to the translation of code into an unfamiliar language.
Our study delves into the capacity of LLMs to mimic human learning processes, offering an in-depth evaluation of our methodology for converting smart contracts written in Solidity to Move.
arXiv Detail & Related papers (2024-03-13T18:55:20Z) - Language-Specific Neurons: The Key to Multilingual Capabilities in Large Language Models [117.20416338476856]
Large language models (LLMs) demonstrate remarkable multilingual capabilities without being pre-trained on specially curated multilingual parallel corpora.
We propose a novel detection method, language activation probability entropy (LAPE), to identify language-specific neurons within LLMs.
Our findings indicate that LLMs' proficiency in processing a particular language is predominantly due to a small subset of neurons.
arXiv Detail & Related papers (2024-02-26T09:36:05Z) - If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code
Empowers Large Language Models to Serve as Intelligent Agents [81.60906807941188]
Large language models (LLMs) are trained on a combination of natural language and formal language (code)
Code translates high-level goals into executable steps, featuring standard syntax, logical consistency, abstraction, and modularity.
arXiv Detail & Related papers (2024-01-01T16:51:20Z) - Okapi: Instruction-tuned Large Language Models in Multiple Languages
with Reinforcement Learning from Human Feedback [61.83548032416181]
We present Okapi, the first system with instruction-tuned LLMs based on RLHF for multiple languages.
Okapi introduces instruction and response-ranked data in 26 diverse languages to facilitate the experiments and development of future multilingual LLM research.
arXiv Detail & Related papers (2023-07-29T18:01:46Z)
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.