Search Results for author:
Found 32 papers, 19 papers with code
LLoCO: Learning Long Contexts Offline
We introduce LLoCO, a technique that combines context compression, retrieval, and parameter-efficient finetuning using LoRA.
GoEX: Perspectives and Designs Towards a Runtime for Autonomous LLM Applications
We believe this is critical to unlock the potential for LLM agents to interact with applications and services with limited (post-facto) human involvement.
RAFT: Adapting Language Model to Domain Specific RAG
In this paper, we present Retrieval Augmented FineTuning (RAFT), a training recipe that improves the model's ability to answer questions in a "open-book" in-domain settings.
LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code
Large Language Models (LLMs) applied to code-related applications have emerged as a prominent field, attracting significant interest from both academia and industry.
In-Context Principle Learning from Mistakes
We evaluate LEAP on a wide range of benchmarks, including multi-hop question answering (Hotpot QA), textual QA (DROP), Big-Bench Hard reasoning, and math problems (GSM8K and MATH); in all these benchmarks, LEAP improves the strongest available LLMs such as GPT-3. 5-turbo, GPT-4, GPT-4 turbo and Claude-2. 1.
Iterative Prompt Relabeling for diffusion model with RLDF
IP-RLDF first samples a batch of images conditioned on the text, then relabels the text prompts of unmatched text-image pairs with classifier feedback.
LLM-Assisted Code Cleaning For Training Accurate Code Generators
In this work, we investigate data quality for code and find that making the code more structured and readable leads to improved code generation performance of the system.
AgentBench: Evaluating LLMs as Agents
We present AgentBench, a multi-dimensional evolving benchmark that currently consists of 8 distinct environments to assess LLM-as-Agent's reasoning and decision-making abilities in a multi-turn open-ended generation setting.
Controllable Text-to-Image Generation with GPT-4
Control-GPT works by querying GPT-4 to write TikZ code, and the generated sketches are used as references alongside the text instructions for diffusion models (e. g., ControlNet) to generate photo-realistic images.
Gorilla: Large Language Model Connected with Massive APIs
Large Language Models (LLMs) have seen an impressive wave of advances recently, with models now excelling in a variety of tasks, such as mathematical reasoning and program synthesis.
The Wisdom of Hindsight Makes Language Models Better Instruction Followers
In this paper, we consider an alternative approach: converting feedback to instruction by relabeling the original one and training the model for better alignment in a supervised manner.
Latent Variable Representation for Reinforcement Learning
Theoretically, we establish the sample complexity of the proposed approach in the online and offline settings.
Model-based Reinforcement Learning
reinforcement-learning
+1
Multitask Vision-Language Prompt Tuning
Specifically, (i) we demonstrate the effectiveness of learning a single transferable prompt from multiple source tasks to initialize the prompt for each target task; (ii) we show many target tasks can benefit each other from sharing prompt vectors and thus can be jointly learned via multitask prompt tuning.
TEMPERA: Test-Time Prompting via Reinforcement Learning
To achieve this, we design a novel action space that allows flexible editing of the initial prompts covering a wide set of commonly-used components like instructions, few-shot exemplars, and verbalizers.
Efficient Planning in a Compact Latent Action Space
Planning-based reinforcement learning has shown strong performance in tasks in discrete and low-dimensional continuous action spaces.
Spectral Decomposition Representation for Reinforcement Learning
Representation learning often plays a critical role in reinforcement learning by managing the curse of dimensionality.
Making Linear MDPs Practical via Contrastive Representation Learning
It is common to address the curse of dimensionality in Markov decision processes (MDPs) by exploiting low-rank representations.
Contrastive Learning as Goal-Conditioned Reinforcement Learning
While deep RL should automatically acquire such good representations, prior work often finds that learning representations in an end-to-end fashion is unstable and instead equip RL algorithms with additional representation learning parts (e. g., auxiliary losses, data augmentation).
Graph Backup: Data Efficient Backup Exploiting Markovian Transitions
In this paper, we treat the transition data of the MDP as a graph, and define a novel backup operator, Graph Backup, which exploits this graph structure for better value estimation.
NovelD: A Simple yet Effective Exploration Criterion
We analyze NovelD thoroughly in MiniGrid and found that empirically it helps the agent explore the environment more uniformly with a focus on exploring beyond the boundary.
A Free Lunch from the Noise: Provable and Practical Exploration for Representation Learning
Representation learning lies at the heart of the empirical success of deep learning for dealing with the curse of dimensionality.
C-Planning: An Automatic Curriculum for Learning Goal-Reaching Tasks
Goal-conditioned reinforcement learning (RL) can solve tasks in a wide range of domains, including navigation and manipulation, but learning to reach distant goals remains a central challenge to the field.
Multi-objective Optimization by Learning Space Partitions
In this paper, we propose LaMOO, a novel multi-objective optimizer that learns a model from observed samples to partition the search space and then focus on promising regions that are likely to contain a subset of the Pareto frontier.
Multi-objective Optimization by Learning Space Partition
In this paper, we propose LaMOO, a novel multi-objective optimizer that learns a model from observed samples to partition the search space and then focus on promising regions that are likely to contain a subset of the Pareto frontier.
Learning Space Partitions for Path Planning
Path planning, the problem of efficiently discovering high-reward trajectories, often requires optimizing a high-dimensional and multimodal reward function.
MADE: Exploration via Maximizing Deviation from Explored Regions
As a proof of concept, we evaluate the new intrinsic reward on tabular examples across a variety of model-based and model-free algorithms, showing improvements over count-only exploration strategies.
BeBold: Exploration Beyond the Boundary of Explored Regions
In this paper, we analyze the pros and cons of each method and propose the regulated difference of inverse visitation counts as a simple but effective criterion for IR.
Multi-Agent Collaboration via Reward Attribution Decomposition
In this work, we propose Collaborative Q-learning (CollaQ) that achieves state-of-the-art performance in the StarCraft multi-agent challenge and supports ad hoc team play.
Contrastive Code Representation Learning
Recent work learns contextual representations of source code by reconstructing tokens from their context.
Ranked #1 on
Method name prediction
on CodeSearchNet
ANODEV2: A Coupled Neural ODE Framework
It has been observed that residual networks can be viewed as the explicit Euler discretization of an Ordinary Differential Equation (ODE).
ANODEV2: A Coupled Neural ODE Evolution Framework
It has been observed that residual networks can be viewed as the explicit Euler discretization of an Ordinary Differential Equation (ODE).
Synetgy: Algorithm-hardware Co-design for ConvNet Accelerators on Embedded FPGAs
DiracDeltaNet achieves competitive accuracy on ImageNet (88. 7\% top-5), but with 42$\times$ fewer parameters and 48$\times$ fewer OPs than VGG16.
