Lemur: Harmonizing Natural Language and Code for Language Agents
- URL: http://arxiv.org/abs/2310.06830v2
- Date: Sat, 24 Aug 2024 21:30:00 GMT
- Title: Lemur: Harmonizing Natural Language and Code for Language Agents
- Authors: Yiheng Xu, Hongjin Su, Chen Xing, Boyu Mi, Qian Liu, Weijia Shi, Binyuan Hui, Fan Zhou, Yitao Liu, Tianbao Xie, Zhoujun Cheng, Siheng Zhao, Lingpeng Kong, Bailin Wang, Caiming Xiong, Tao Yu,
- Abstract summary: We introduce Lemur and Lemur-Chat, open-source language models optimized for both natural language and coding capabilities.
Our models achieve state-of-the-art averaged performance across diverse text and coding benchmarks.
The harmonization between natural and programming languages enables Lemur-Chat to significantly narrow the gap with proprietary models on agent abilities.
- Score: 105.43564788499901
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We introduce Lemur and Lemur-Chat, openly accessible language models optimized for both natural language and coding capabilities to serve as the backbone of versatile language agents. The evolution from language chat models to functional language agents demands that models not only master human interaction, reasoning, and planning but also ensure grounding in the relevant environments. This calls for a harmonious blend of language and coding capabilities in the models. Lemur and Lemur-Chat are proposed to address this necessity, demonstrating balanced proficiencies in both domains, unlike existing open-source models that tend to specialize in either. Through meticulous pre-training using a code-intensive corpus and instruction fine-tuning on text and code data, our models achieve state-of-the-art averaged performance across diverse text and coding benchmarks among open-source models. Comprehensive experiments demonstrate Lemur's superiority over existing open-source models and its proficiency across various agent tasks involving human communication, tool usage, and interaction under fully- and partially- observable environments. The harmonization between natural and programming languages enables Lemur-Chat to significantly narrow the gap with proprietary models on agent abilities, providing key insights into developing advanced open-source agents adept at reasoning, planning, and operating seamlessly across environments. https://github.com/OpenLemur/Lemur
Related papers
- CMULAB: An Open-Source Framework for Training and Deployment of Natural Language Processing Models [59.91221728187576]
This paper introduces the CMU Linguistic Linguistic Backend, an open-source framework that simplifies model deployment and continuous human-in-the-loop fine-tuning of NLP models.
CMULAB enables users to leverage the power of multilingual models to quickly adapt and extend existing tools for speech recognition, OCR, translation, and syntactic analysis to new languages.
arXiv Detail & Related papers (2024-04-03T02:21:46Z) - Do Machines and Humans Focus on Similar Code? Exploring Explainability
of Large Language Models in Code Summarization [10.201463330812167]
We report negative results from our investigation of explainability of language models in code summarization through the lens of human comprehension.
We employ a state-of-the-art model-agnostic, black-box, perturbation-based approach, SHAP, to identify which code tokens influence that generation of summaries.
Our study highlights an inability to align human focus with SHAP-based model focus measures.
arXiv Detail & Related papers (2024-02-22T00:01:02Z) - Qwen Technical Report [132.54304067403922]
We introduce Qwen, the first installment of our large language model series.
Qwen is the base pretrained language models, and Qwen-Chat, the chat models finetuned with human alignment techniques.
We have also developed coding-specialized models, Code-Qwen and Code-Qwen-Chat, as well as mathematics-focused models, Math-Qwen-Chat.
arXiv Detail & Related papers (2023-09-28T17:07:49Z) - Diffusion Language Models Can Perform Many Tasks with Scaling and
Instruction-Finetuning [56.03057119008865]
We show that scaling diffusion language models can effectively make them strong language learners.
We build competent diffusion language models at scale by first acquiring knowledge from massive data.
Experiments show that scaling diffusion language models consistently improves performance across downstream language tasks.
arXiv Detail & Related papers (2023-08-23T16:01:12Z) - ChatDev: Communicative Agents for Software Development [84.90400377131962]
ChatDev is a chat-powered software development framework in which specialized agents are guided in what to communicate.
These agents actively contribute to the design, coding, and testing phases through unified language-based communication.
arXiv Detail & Related papers (2023-07-16T02:11:34Z) - Language Models are General-Purpose Interfaces [109.45478241369655]
We propose to use language models as a general-purpose interface to various foundation models.
A collection of pretrained encoders perceive diverse modalities (such as vision, and language)
We propose a semi-causal language modeling objective to jointly pretrain the interface and the modular encoders.
arXiv Detail & Related papers (2022-06-13T17:34:22Z) - Language Models are not Models of Language [0.0]
Transfer learning has enabled large deep learning neural networks trained on the language modeling task to vastly improve performance.
We argue that the term language model is misleading because deep learning models are not theoretical models of language.
arXiv Detail & Related papers (2021-12-13T22:39:46Z) - Evaluating Cross-Lingual Transfer Learning Approaches in Multilingual
Conversational Agent Models [1.52292571922932]
We propose a general multilingual model framework for Natural Language Understanding (NLU) models.
We show that these multilingual models can reach same or better performance compared to monolingual models across language-specific test data.
arXiv Detail & Related papers (2020-12-07T17:14:52Z) - Multi-agent Communication meets Natural Language: Synergies between
Functional and Structural Language Learning [16.776753238108036]
We present a method for combining multi-agent communication and traditional data-driven approaches to natural language learning.
Our starting point is a language model that has been trained on generic, not task-specific language data.
We then place this model in a multi-agent self-play environment that generates task-specific rewards used to adapt or modulate the model.
arXiv Detail & Related papers (2020-05-14T15:32:23Z)
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.