Search Results for author:
Found 8 papers, 2 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.
CodeFort: Robust Training for Code Generation Models
Code generation models are not robust to small perturbations, which often lead to incorrect generations and significantly degrade the performance of these models.
Bifurcated Attention: Accelerating Massively Parallel Decoding with Shared Prefixes in LLMs
This study introduces bifurcated attention, a method designed to enhance language model inference in shared-context batch decoding scenarios.
Token Alignment via Character Matching for Subword Completion
Generative models, widely utilized in various applications, can often struggle with prompts corresponding to partial tokens.
Constrained Decoding for Fill-in-the-Middle Code Language Models via Efficient Left and Right Quotienting of Context-Sensitive Grammars
Large Language Models are powerful tools for program synthesis and advanced auto-completion, but come with no guarantee that their output code is syntactically correct.
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.
The 2019 BBN Cross-lingual Information Retrieval System
In this paper, we describe a cross-lingual information retrieval (CLIR) system that, given a query in English, and a set of audio and text documents in a foreign language, can return a scored list of relevant documents, and present findings in a summary form in English.
Speech Recognition: Keyword Spotting Through Image Recognition
The problem of identifying voice commands has always been a challenge due to the presence of noise and variability in speed, pitch, etc.
