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Found 7 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.
Token Alignment via Character Matching for Subword Completion
Generative models, widely utilized in various applications, can often struggle with prompts corresponding to partial tokens.
Bifurcated Attention for Single-Context Large-Batch Sampling
In our study, we present bifurcated attention, a method developed for language model inference in single-context batch sampling contexts.
Constrained Decoding for 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.
