Search Results for author:
Found 7 papers, 3 papers with code
Asynchronous Convergence in Multi-Task Learning via Knowledge Distillation from Converged Tasks
However, one issue that often arises in MTL is the convergence speed between tasks varies due to differences in task difficulty, so it can be a challenge to simultaneously achieve the best performance on all tasks with a single model checkpoint.
Shopping MMLU: A Massive Multi-Task Online Shopping Benchmark for Large Language Models
Shopping MMLU consists of 57 tasks covering 4 major shopping skills: concept understanding, knowledge reasoning, user behavior alignment, and multi-linguality, and can thus comprehensively evaluate the abilities of LLMs as general shop assistants.
Evolutionary Contrastive Distillation for Language Model Alignment
The ability of large language models (LLMs) to execute complex instructions is essential for their real-world applications.
Large Language Models in the Clinic: A Comprehensive Benchmark
The adoption of large language models (LLMs) to assist clinicians has attracted remarkable attention.
DynaMaR: Dynamic Prompt with Mask Token Representation
In this paper, we propose an improvement to prompt-based fine-tuning that addresses these two issues.
Embracing Structure in Data for Billion-Scale Semantic Product Search
When training a dyadic model, one seeks to embed two different types of entities (e. g., queries and documents or users and movies) in a common vector space such that pairs with high relevance are positioned nearby.
Semantic Product Search
To address these issues, we train a deep learning model for semantic matching using customer behavior data.
