Search Results for author:
Found 23 papers, 17 papers with code
Towards Coarse-to-Fine Evaluation of Inference Efficiency for Large Language Models
In real world, large language models (LLMs) can serve as the assistant to help users accomplish their jobs, and also support the development of advanced applications.
Language-Specific Neurons: The Key to Multilingual Capabilities in Large Language Models
Large language models (LLMs) demonstrate remarkable multilingual capabilities without being pre-trained on specially curated multilingual parallel corpora.
Beyond Imitation: Leveraging Fine-grained Quality Signals for Alignment
Alignment with human preference is a desired property of large language models (LLMs).
BAMBOO: A Comprehensive Benchmark for Evaluating Long Text Modeling Capacities of Large Language Models
Recently, multiple studies have committed to extending the context length and enhancing the long text modeling capabilities of LLMs.
Towards Effective Ancient Chinese Translation: Dataset, Model, and Evaluation
From an evaluation perspective, we build a benchmark to judge ancient Chinese translation quality in different scenarios and evaluate the ancient Chinese translation capacities of various existing models.
Zero-shot Visual Question Answering with Language Model Feedback
In this paper, we propose a novel language model guided captioning approach, LAMOC, for knowledge-based visual question answering (VQA).
Learning to Imagine: Visually-Augmented Natural Language Generation
People often imagine relevant scenes to aid in the writing process.
Not All Metrics Are Guilty: Improving NLG Evaluation by Diversifying References
Most research about natural language generation (NLG) relies on evaluation benchmarks with limited references for a sample, which may result in poor correlations with human judgements.
The Web Can Be Your Oyster for Improving Large Language Models
In order to further improve the capacity of LLMs for knowledge-intensive tasks, we consider augmenting LLMs with the large-scale web using search engine.
Not All Languages Are Created Equal in LLMs: Improving Multilingual Capability by Cross-Lingual-Thought Prompting
Large language models (LLMs) demonstrate impressive multilingual capability, but their performance varies substantially across different languages.
A Survey of Large Language Models
To discriminate the difference in parameter scale, the research community has coined the term large language models (LLM) for the PLMs of significant size.
A Survey on Long Text Modeling with Transformers
In this paper, we provide an overview of the recent advances on long texts modeling based on Transformer models.
TextBox 2.0: A Text Generation Library with Pre-trained Language Models
To facilitate research on text generation, this paper presents a comprehensive and unified library, TextBox 2. 0, focusing on the use of pre-trained language models (PLMs).
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ELMER: A Non-Autoregressive Pre-trained Language Model for Efficient and Effective Text Generation
However, NAR models usually generate texts of lower quality due to the absence of token dependency in the output text.
MVP: Multi-task Supervised Pre-training for Natural Language Generation
Motivated by the success of supervised pre-training, we propose Multi-task superVised Pre-training (MVP) for natural language generation.
ElitePLM: An Empirical Study on General Language Ability Evaluation of Pretrained Language Models
In this paper, we present a large-scale empirical study on general language ability evaluation of PLMs (ElitePLM).
Learning to Transfer Prompts for Text Generation
First, PTG learns a set of source prompts for various source generation tasks and then transfers these prompts as target prompts to perform target generation tasks.
Context-Tuning: Learning Contextualized Prompts for Natural Language Generation
Secondly, we use continuous inverse prompting to improve the process of natural language generation by modeling an inverse generation process from output to input, making the generated text more relevant to the inputs.
Pretrained Language Models for Text Generation: A Survey
We begin with introducing three key aspects of applying PLMs to text generation: 1) how to encode the input into representations preserving input semantics which can be fused into PLMs; 2) how to design an effective PLM to serve as the generation model; and 3) how to effectively optimize PLMs given the reference text and to ensure that the generated texts satisfy special text properties.
Few-shot Knowledge Graph-to-Text Generation with Pretrained Language Models
This paper studies how to automatically generate a natural language text that describes the facts in knowledge graph (KG).
Pretrained Language Models for Text Generation: A Survey
In this paper, we present an overview of the major advances achieved in the topic of PLMs for text generation.
Session-based Social and Dependency-aware Software Recommendation
With the increase of complexity of modern software, social collaborative coding and reuse of open source software packages become more and more popular, which thus greatly enhances the development efficiency and software quality.
TextBox: A Unified, Modularized, and Extensible Framework for Text Generation
In this paper, we release an open-source library, called TextBox, to provide a unified, modularized, and extensible text generation framework.
