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Found 14 papers, 3 papers with code
Domain-Lifelong Learning for Dialogue State Tracking via Knowledge Preservation Networks
This paradigm is often impractical in real-world applications since online dialogue systems usually involve continually emerging new data and domains.
Not All Contexts Are Equal: Teaching LLMs Credibility-aware Generation
In this paper, we propose Credibility-aware Generation (CAG), a universally applicable framework designed to mitigate the impact of flawed information in RAG.
What Makes Quantization for Large Language Models Hard? An Empirical Study from the Lens of Perturbation
Our findings reveal several connections between the properties of perturbations and LLM performance, providing insights into the failure cases of uniform quantization and suggesting potential solutions to improve the robustness of LLM quantization.
Unraveling the Mystery of Scaling Laws: Part I
However, the original scaling law paper by OpenAI did not disclose the complete details necessary to derive the precise scaling law formulas, and their conclusions are only based on models containing up to 1. 5 billion parameters.
Learning or Self-aligning? Rethinking Instruction Fine-tuning
Instruction Fine-tuning~(IFT) is a critical phase in building large language models~(LLMs).
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.
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.
Improving Input-label Mapping with Demonstration Replay for In-context Learning
The effectiveness of ICL can be attributed to the strong language modeling capabilities of large language models (LLMs), which enable them to learn the mapping between input and labels based on in-context demonstrations.
Retrieval-based Knowledge Transfer: An Effective Approach for Extreme Large Language Model Compression
In particular, our approach extracts knowledge from LLMs to construct a knowledge store, from which the small-scale model can retrieve relevant information and leverage it for effective inference.
APP: Adaptive Prototypical Pseudo-Labeling for Few-shot OOD Detection
Detecting out-of-domain (OOD) intents from user queries is essential for a task-oriented dialogue system.
Large Language Models Meet Open-World Intent Discovery and Recognition: An Evaluation of ChatGPT
The tasks of out-of-domain (OOD) intent discovery and generalized intent discovery (GID) aim to extend a closed intent classifier to open-world intent sets, which is crucial to task-oriented dialogue (TOD) systems.
Confidence Calibration for Intent Detection via Hyperspherical Space and Rebalanced Accuracy-Uncertainty Loss
Experiments on the open datasets verify that our model outperforms the existing calibration methods and achieves a significant improvement on the calibration metric.
Density-Based Dynamic Curriculum Learning for Intent Detection
To handle this problem, we propose a density-based dynamic curriculum learning model.
From Paraphrasing to Semantic Parsing: Unsupervised Semantic Parsing via Synchronous Semantic Decoding
During synchronous decoding: the utterance paraphrasing is constrained by the structure of the logical form, therefore the canonical utterance can be paraphrased controlledly; the semantic decoding is guided by the semantics of the canonical utterance, therefore its logical form can be generated unsupervisedly.
