Search Results for author:
Found 11 papers, 8 papers with code
MMTEB: Massive Multilingual Text Embedding Benchmark
MMTEB includes a diverse set of challenging, novel tasks such as instruction following, long-document retrieval, and code retrieval, representing the largest multilingual collection of evaluation tasks for embedding models to date.
Learn-by-interact: A Data-Centric Framework for Self-Adaptive Agents in Realistic Environments
Autonomous agents powered by large language models (LLMs) have the potential to enhance human capabilities, assisting with digital tasks from sending emails to performing data analysis.
Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows
Real-world enterprise text-to-SQL workflows often involve complex cloud or local data across various database systems, multiple SQL queries in various dialects, and diverse operations from data transformation to analytics.
Ranked #1 on
Text-To-SQL
on Spider 2.0
BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval
To better benchmark retrieval on such challenging queries, we introduce BRIGHT, the first text retrieval benchmark that requires intensive reasoning to retrieve relevant documents.
EVOR: Evolving Retrieval for Code Generation
Recently the retrieval-augmented generation (RAG) has been successfully applied in code generation.
Generative Representational Instruction Tuning
Notably, we find that GRIT matches training on only generative or embedding data, thus we can unify both at no performance loss.
OpenAgents: An Open Platform for Language Agents in the Wild
Language agents show potential in being capable of utilizing natural language for varied and intricate tasks in diverse environments, particularly when built upon large language models (LLMs).
Lemur: Harmonizing Natural Language and Code for Language Agents
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.
One Embedder, Any Task: Instruction-Finetuned Text Embeddings
Our analysis suggests that INSTRUCTOR is robust to changes in instructions, and that instruction finetuning mitigates the challenge of training a single model on diverse datasets.
Selective Annotation Makes Language Models Better Few-Shot Learners
Departing from recent in-context learning methods, we formulate an annotation-efficient, two-step framework: selective annotation that chooses a pool of examples to annotate from unlabeled data in advance, followed by prompt retrieval that retrieves task examples from the annotated pool at test time.
Taming Pre-trained Language Models with N-gram Representations for Low-Resource Domain Adaptation
In this paper, we aim to adapt a generic pretrained model with a relatively small amount of domain-specific data.
Ranked #42 on
Time Series Forecasting
on ETTh1 (336) Multivariate
