Search Results for author:
Found 14 papers, 3 papers with code
BASS: Batched Attention-optimized Speculative Sampling
Speculative decoding has emerged as a powerful method to improve latency and throughput in hosting large language models.
Token Alignment via Character Matching for Subword Completion
Generative models, widely utilized in various applications, can often struggle with prompts corresponding to partial tokens.
Code-Aware Prompting: A study of Coverage Guided Test Generation in Regression Setting using LLM
Recent works using large language models (LLMs) for test generation have focused on improving generation quality through optimizing the test generation context and correcting errors in model outputs, but use fixed prompting strategies that prompt the model to generate tests without additional guidance.
Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learning
We present a novel approach for structured data-to-text generation that addresses the limitations of existing methods that primarily focus on specific types of structured data.
Greener yet Powerful: Taming Large Code Generation Models with Quantization
Such large models incur significant resource usage (in terms of memory, latency, and dollars) as well as carbon footprint.
ReCode: Robustness Evaluation of Code Generation Models
Most existing works on robustness in text or code tasks have focused on classification, while robustness in generation tasks is an uncharted area and to date there is no comprehensive benchmark for robustness in code generation.
Multi-lingual Evaluation of Code Generation Models
Using these benchmarks, we are able to assess the performance of code generation models in a multi-lingual fashion, and discovered generalization ability of language models on out-of-domain languages, advantages of multi-lingual models over mono-lingual, the ability of few-shot prompting to teach the model new languages, and zero-shot translation abilities even on mono-lingual settings.
Entity Resolution in Open-domain Conversations
In recent years, incorporating external knowledge for response generation in open-domain conversation systems has attracted great interest.
Ape210K: A Large-Scale and Template-Rich Dataset of Math Word Problems
We propose a copy-augmented and feature-enriched sequence to sequence (seq2seq) model, which outperforms existing models by 3. 2% on the Math23K dataset and serves as a strong baseline of the Ape210K dataset.
Who Is Speaking to Whom? Learning to Identify Utterance Addressee in Multi-Party Conversations
Previous research on dialogue systems generally focuses on the conversation between two participants, yet multi-party conversations which involve more than two participants within one session bring up a more complicated but realistic scenario.
Semi-supervised Text Style Transfer: Cross Projection in Latent Space
Text style transfer task requires the model to transfer a sentence of one style to another style while retaining its original content meaning, which is a challenging problem that has long suffered from the shortage of parallel data.
Find a Reasonable Ending for Stories: Does Logic Relation Help the Story Cloze Test?
In this paper, we incorporate the logic information with the help of the Natural Language Inference (NLI) task to the Story Cloze Test (SCT).
One "Ruler" for All Languages: Multi-Lingual Dialogue Evaluation with Adversarial Multi-Task Learning
Automatic evaluating the performance of Open-domain dialogue system is a challenging problem.
Tree2Tree Learning with Memory Unit
Traditional recurrent neural network (RNN) or convolutional neural net- work (CNN) based sequence-to-sequence model can not handle tree structural data well.
