Search Results for author:
Found 18 papers, 1 papers with code
Counterfactual Off-Policy Training for Neural Dialogue Generation
Open-domain dialogue generation suffers from the data insufficiency problem due to the vast size of potential responses.
Semi-Instruct: Bridging Natural-Instruct and Self-Instruct for Code Large Language Models
Presently, two dominant paradigms for collecting tuning data are natural-instruct (human-written) and self-instruct (automatically generated).
OneBit: Towards Extremely Low-bit Large Language Models
Model quantification uses low bit-width values to represent the weight matrices of models, which is a promising approach to reduce both storage and computational overheads of deploying highly anticipated LLMs.
Exploring Hybrid Question Answering via Program-based Prompting
Question answering over heterogeneous data requires reasoning over diverse sources of data, which is challenging due to the large scale of information and organic coupling of heterogeneous data.
Improving Demonstration Diversity by Human-Free Fusing for Text-to-SQL
Currently, the in-context learning method based on large language models (LLMs) has become the mainstream of text-to-SQL research.
MultiPoT: Multilingual Program of Thoughts Harnesses Multiple Programming Languages
In this paper, we conduct comprehensive experiments on the programming languages used in PoT and find that no single language consistently delivers optimal performance across all tasks and models.
Enhancing Numerical Reasoning with the Guidance of Reliable Reasoning Processes
Numerical reasoning is an essential ability for NLP systems to handle numeric information.
Multi-Hop Table Retrieval for Open-Domain Text-to-SQL
To reduce the effect of the similar irrelevant entity, our method focuses on unretrieved entities at each hop and considers the low-ranked tables by beam search.
A Survey of Table Reasoning with Large Language Models
In this paper, we analyze the mainstream techniques used to improve table reasoning performance in the LLM era, and the advantages of LLMs compared to pre-LLMs for solving table reasoning.
MixPro: Simple yet Effective Data Augmentation for Prompt-based Learning
Prompt-based learning has shown considerable promise in reformulating various downstream tasks as cloze problems by combining original input with a predetermined template.
Semantic-Guided Generative Image Augmentation Method with Diffusion Models for Image Classification
In contrast, generative methods bring more image diversity in the augmented images but may not preserve semantic consistency, thus incorrectly changing the essential semantics of the original image.
A Survey on Natural Language Processing for Programming
Natural language processing for programming aims to use NLP techniques to assist programming.
Neural Stylistic Response Generation with Disentangled Latent Variables
Generating open-domain conversational responses in the desired style usually suffers from the lack of parallel data in the style.
Counterfactual Off-Policy Training for Neural Response Generation
Open-domain dialogue generation suffers from the data insufficiency problem due to the vast size of potential responses.
Retrieval-Enhanced Adversarial Training for Neural Response Generation
Dialogue systems are usually built on either generation-based or retrieval-based approaches, yet they do not benefit from the advantages of different models.
Context-Sensitive Generation of Open-Domain Conversational Responses
Despite the success of existing works on single-turn conversation generation, taking the coherence in consideration, human conversing is actually a context-sensitive process.
Neural Personalized Response Generation as Domain Adaptation
Moreover, the lexical divergence of the responses generated by the 5 personalized models indicates that the proposed two-phase approach achieves good results on modeling the responding style of human and generating personalized responses for the conversational systems.
Learning to Start for Sequence to Sequence Architecture
An obvious drawback of these work is that there is not a learnable relationship between words and the start symbol.
