Search Results for author:
Found 26 papers, 16 papers with code
Auto-Intent: Automated Intent Discovery and Self-Exploration for Large Language Model Web Agents
In this paper, we introduce Auto-Intent, a method to adapt a pre-trained large language model (LLM) as an agent for a target domain without direct fine-tuning, where we empirically focus on web navigation tasks.
SPRIG: Improving Large Language Model Performance by System Prompt Optimization
Past research has focused on optimizing prompts specific to a task.
Understanding the Capabilities and Limitations of Large Language Models for Cultural Commonsense
Large language models (LLMs) have demonstrated substantial commonsense understanding through numerous benchmark evaluations.
Small Language Models Need Strong Verifiers to Self-Correct Reasoning
Self-correction has emerged as a promising solution to boost the reasoning performance of large language models (LLMs), where LLMs refine their solutions using self-generated critiques that pinpoint the errors.
AutoGuide: Automated Generation and Selection of State-Aware Guidelines for Large Language Model Agents
The primary limitation of large language models (LLMs) is their restricted understanding of the world.
TOD-Flow: Modeling the Structure of Task-Oriented Dialogues
Our TOD-Flow graph learns what a model can, should, and should not predict, effectively reducing the search space and providing a rationale for the model's prediction.
Code Models are Zero-shot Precondition Reasoners
One of the fundamental skills required for an agent acting in an environment to complete tasks is the ability to understand what actions are plausible at any given point.
When "A Helpful Assistant" Is Not Really Helpful: Personas in System Prompts Do Not Improve Performances of Large Language Models
In this study, we present a systematic evaluation of personas in system prompts.
You don't need a personality test to know these models are unreliable: Assessing the Reliability of Large Language Models on Psychometric Instruments
The versatility of Large Language Models (LLMs) on natural language understanding tasks has made them popular for research in social sciences.
MultiPrompter: Cooperative Prompt Optimization with Multi-Agent Reinforcement Learning
Recently, there has been an increasing interest in automated prompt optimization based on reinforcement learning (RL).
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Merging Generated and Retrieved Knowledge for Open-Domain QA
Open-domain question answering (QA) systems are often built with retrieval modules.
Exploring Demonstration Ensembling for In-context Learning
The standard approach for ICL is to prompt the LM with concatenated demonstrations followed by the test input.
GRACE: Discriminator-Guided Chain-of-Thought Reasoning
To address this issue, we propose Guiding chain-of-thought ReAsoning with a CorrectnEss Discriminator (GRACE), a stepwise decoding approach that steers the decoding process towards producing correct reasoning steps.
A Picture is Worth a Thousand Words: Language Models Plan from Pixels
Planning is an important capability of artificial agents that perform long-horizon tasks in real-world environments.
Unsupervised Task Graph Generation from Instructional Video Transcripts
This work explores the problem of generating task graphs of real-world activities.
Multimodal Subtask Graph Generation from Instructional Videos
Real-world tasks consist of multiple inter-dependent subtasks (e. g., a dirty pan needs to be washed before it can be used for cooking).
Exploring the Benefits of Training Expert Language Models over Instruction Tuning
Recently, Language Models (LMs) instruction-tuned on multiple tasks, also known as multitask-prompted fine-tuning (MT), have shown the capability to generalize to unseen tasks.
Ranked #9 on
Question Answering
on StoryCloze
Knowledge Unlearning for Mitigating Privacy Risks in Language Models
Pretrained Language Models (LMs) memorize a vast amount of knowledge during initial pretraining, including information that may violate the privacy of personal lives and identities.
Ranked #3 on
Language Modelling
on The Pile
(Test perplexity metric)
Few-shot Subgoal Planning with Language Models
Pre-trained large language models have shown successful progress in many language understanding benchmarks.
Few-shot Reranking for Multi-hop QA via Language Model Prompting
To alleviate the need for a large number of labeled question-document pairs for retriever training, we propose PromptRank, which relies on large language models prompting for multi-hop path reranking.
Few-shot Sequence Learning with Transformers
In the simplest setting, we append a token to an input sequence which represents the particular task to be undertaken, and show that the embedding of this token can be optimized on the fly given few labeled examples.
Zero-Shot Entity Linking by Reading Entity Descriptions
First, we show that strong reading comprehension models pre-trained on large unlabeled data can be used to generalize to unseen entities.
An efficient framework for learning sentence representations
In this work we propose a simple and efficient framework for learning sentence representations from unlabelled data.
Sentence Ordering and Coherence Modeling using Recurrent Neural Networks
Modeling the structure of coherent texts is a key NLP problem.
Generative Adversarial Text to Image Synthesis
Automatic synthesis of realistic images from text would be interesting and useful, but current AI systems are still far from this goal.
