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Found 9 papers, 4 papers with code
How Do Your Code LLMs Perform? Empowering Code Instruction Tuning with High-Quality Data
Based on our selected data, we present XCoder, a family of models finetuned from LLaMA3.
CS-Bench: A Comprehensive Benchmark for Large Language Models towards Computer Science Mastery
Computer Science (CS) stands as a testament to the intricacies of human intelligence, profoundly advancing the development of artificial intelligence and modern society.
DivTOD: Unleashing the Power of LLMs for Diversifying Task-Oriented Dialogue Representations
Language models pre-trained on general text have achieved impressive results in diverse fields.
BootTOD: Bootstrap Task-oriented Dialogue Representations by Aligning Diverse Responses
Pre-trained language models have been successful in many scenarios.
Beyond the Known: Investigating LLMs Performance on Out-of-Domain Intent Detection
Out-of-domain (OOD) intent detection aims to examine whether the user's query falls outside the predefined domain of the system, which is crucial for the proper functioning of task-oriented dialogue (TOD) systems.
PreAct: Predicting Future in ReAct Enhances Agent's Planning Ability
Addressing the discrepancies between predictions and actual outcomes often aids individuals in expanding their thought processes and engaging in reflection, thereby facilitating reasoning in the correct direction.
Multi-Perspective Consistency Enhances Confidence Estimation in Large Language Models
In this work, we focus on improving the confidence estimation of large language models.
DolphCoder: Echo-Locating Code Large Language Models with Diverse and Multi-Objective Instruction Tuning
It learns diverse instruction targets and combines a code evaluation objective to enhance its code generation ability.
FutureTOD: Teaching Future Knowledge to Pre-trained Language Model for Task-Oriented Dialogue
Pre-trained language models based on general text enable huge success in the NLP scenario.
