Search Results for author:
Found 9 papers, 6 papers with code
Soft Self-Consistency Improves Language Model Agents
Current "sample and select" methods such as self-consistency (SC) rely on majority voting to score answers.
ReGAL: Refactoring Programs to Discover Generalizable Abstractions
While large language models (LLMs) are increasingly being used for program synthesis, they lack the global view needed to develop useful abstractions; they generally predict programs one at a time, often repeating the same functionality.
ADaPT: As-Needed Decomposition and Planning with Language Models
Large Language Models (LLMs) are increasingly being used for interactive decision-making tasks requiring planning and adapting to the environment.
Rephrase, Augment, Reason: Visual Grounding of Questions for Vision-Language Models
An increasing number of vision-language tasks can be handled with little to no training, i. e., in a zero and few-shot manner, by marrying large language models (LLMs) to vision encoders, resulting in large vision-language models (LVLMs).
ReCEval: Evaluating Reasoning Chains via Correctness and Informativeness
Multi-step reasoning ability is fundamental to many natural language tasks, yet it is unclear what constitutes a good reasoning chain and how to evaluate them.
GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models
Providing natural language instructions in prompts is a useful new paradigm for improving task performance of large language models in a zero-shot setting.
The Effectiveness of Intermediate-Task Training for Code-Switched Natural Language Understanding
In this work, we propose the use of bilingual intermediate pretraining as a reliable technique to derive large and consistent performance gains on three different NLP tasks using code-switched text.
An Investigation of End-to-End Models for Robust Speech Recognition
A systematic comparison of these two approaches for end-to-end robust ASR has not been attempted before.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
How Accents Confound: Probing for Accent Information in End-to-End Speech Recognition Systems
We use a state-of-the-art end-to-end ASR system, comprising convolutional and recurrent layers, that is trained on a large amount of US-accented English speech and evaluate the model on speech samples from seven different English accents.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
