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Found 19 papers, 7 papers with code
ToolACE-R: Tool Learning with Adaptive Self-Refinement
Tool learning, which allows Large Language Models (LLMs) to leverage external tools for solving complex user tasks, has emerged as a promising avenue for extending model capabilities.
FReM: A Flexible Reasoning Mechanism for Balancing Quick and Slow Thinking in Long-Context Question Answering
Long-context question-answering (LCQA) systems have greatly benefited from the powerful reasoning capabilities of large language models (LLMs), which can be categorized into slow and quick reasoning modes.
ToolFlow: Boosting LLM Tool-Calling Through Natural and Coherent Dialogue Synthesis
Data quality assessments demonstrate improvements in the naturalness and coherence of our synthesized dialogues.
MlingConf: A Comprehensive Study of Multilingual Confidence Estimation on Large Language Models
This paper addresses this gap by introducing a comprehensive investigation of Multilingual Confidence estimation (MlingConf) on LLMs, focusing on both language-agnostic (LA) and language-specific (LS) tasks to explore the performance and language dominance effects of multilingual confidence estimations on different tasks.
ToolACE: Winning the Points of LLM Function Calling
Function calling significantly extends the application boundary of large language models, where high-quality and diverse training data is critical for unlocking this capability.
Chain-of-Probe: Examing the Necessity and Accuracy of CoT Step-by-Step
To address these questions, we propose a method, namely Chain-of-Probe (CoP), to probe changes in the mind during the model's reasoning.
Gen4DS: Workshop on Data Storytelling in an Era of Generative AI
Storytelling is an ancient and precious human ability that has been rejuvenated in the digital age.
PerLTQA: A Personal Long-Term Memory Dataset for Memory Classification, Retrieval, and Synthesis in Question Answering
This dataset is collected to investigate the use of personalized memories, focusing on social interactions and events in the QA task.
A Comprehensive Study of Multilingual Confidence Estimation on Large Language Models
This paper addresses this gap by introducing a comprehensive investigation of Multilingual Confidence estimation (MlingConf) on LLMs, focusing on both language-agnostic (LA) and language-specific (LS) tasks to explore the performance and language dominance effects of multilingual confidence estimations on different tasks.
UniMS-RAG: A Unified Multi-source Retrieval-Augmented Generation for Personalized Dialogue Systems
To better plan and incorporate the use of multiple sources in generating personalized response, we firstly decompose it into three sub-tasks: Knowledge Source Selection, Knowledge Retrieval, and Response Generation.
Self-Guard: Empower the LLM to Safeguard Itself
Currently, there are two main approaches to address jailbreak attacks: safety training and safeguards.
JoTR: A Joint Transformer and Reinforcement Learning Framework for Dialog Policy Learning
In addition, JoTR employs reinforcement learning with a reward-shaping mechanism to efficiently finetune the word-level dialogue policy, which allows the model to learn from its interactions, improving its performance over time.
Towards Robust Personalized Dialogue Generation via Order-Insensitive Representation Regularization
Generating persona consistent dialogue response is important for developing an intelligent conversational agent.
Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering
Large Language Model (LLM) has gained popularity and achieved remarkable results in open-domain tasks, but its performance in real industrial domain-specific scenarios is average due to its lack of specific domain knowledge.
Cue-CoT: Chain-of-thought Prompting for Responding to In-depth Dialogue Questions with LLMs
Large Language Models (LLMs), such as \texttt{ChatGPT}, greatly empower dialogue systems with strong language understanding and generation capabilities.
Testability-Aware Low Power Controller Design with Evolutionary Learning
The proposed GA-guided XORNets also allows reducing the number of control bits, and the total testing time decreases by 20. 78% on average and up to 47. 09% compared to the existing design without sacrificing test coverage.
Integrating Pretrained Language Model for Dialogue Policy Learning
Reinforcement Learning (RL) has been witnessed its potential for training a dialogue policy agent towards maximizing the accumulated rewards given from users.
Prior Omission of Dissimilar Source Domain(s) for Cost-Effective Few-Shot Learning
To tackle this problem, we propose an effective similarity-based method to select data from the source domains.
MCML: A Novel Memory-based Contrastive Meta-Learning Method for Few Shot Slot Tagging
Meta-learning is widely used for few-shot slot tagging in task of few-shot learning.
