Search Results for author:
Found 15 papers, 12 papers with code
Learning to Generate Unit Tests for Automated Debugging
However, we uncover a trade-off between generating unit test inputs that reveal errors when given a faulty code and correctly predicting the unit test output without access to the gold solution.
Self-Consistency Preference Optimization
Self-alignment, whereby models learn to improve themselves without human annotation, is a rapidly growing research area.
LASeR: Learning to Adaptively Select Reward Models with Multi-Armed Bandits
Our results on commonsense and math reasoning tasks demonstrate that LASeR can boost iterative LLM optimization by optimizing for multiple RMs, improving the absolute average accuracy of Llama-3-8B over three datasets by 2. 67% over training with ensemble RM scores while also showing superior training efficiency (e. g., a 2x speedup).
MAgICoRe: Multi-Agent, Iterative, Coarse-to-Fine Refinement for Reasoning
Moreover, to ensure effective refinement, we employ a multi-agent loop with three agents: Solver, Reviewer (which generates targeted feedback based on step-wise RM scores), and the Refiner (which incorporates feedback).
AdaCAD: Adaptively Decoding to Balance Conflicts between Contextual and Parametric Knowledge
Knowledge conflict arises from discrepancies between information in the context of a large language model (LLM) and the knowledge stored in its parameters.
System-1.x: Learning to Balance Fast and Slow Planning with Language Models
To this end, we propose the System-1. x Planner, a controllable planning framework with LLMs that is capable of generating hybrid plans and balancing between the two planning modes based on the difficulty of the problem at hand.
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
