Search Results for author:
Found 37 papers, 31 papers with code
Pass off Fish Eyes for Pearls: Attacking Model Selection of Pre-trained Models
To validate our viewpoints, we design two methods to evaluate the robustness of FMS: (1) model disguise attack, which post-trains an inferior PTM with a contrastive objective, and (2) evaluation data selection, which selects a subset of the data points for FMS evaluation based on K-means clustering.
StableToolBench: Towards Stable Large-Scale Benchmarking on Tool Learning of Large Language Models
The virtual API server contains a caching system and API simulators which are complementary to alleviate the change in API status.
RepoAgent: An LLM-Powered Open-Source Framework for Repository-level Code Documentation Generation
Generative models have demonstrated considerable potential in software engineering, particularly in tasks such as code generation and debugging.
Large Language Model-based Human-Agent Collaboration for Complex Task Solving
We construct a human-agent collaboration dataset to train this policy model in an offline reinforcement learning environment.
Tell Me More! Towards Implicit User Intention Understanding of Language Model Driven Agents
Current language model-driven agents often lack mechanisms for effective user participation, which is crucial given the vagueness commonly found in user instructions.
UniMem: Towards a Unified View of Long-Context Large Language Models
Although there exist various methods devoted to enhancing the long-context processing ability of large language models (LLMs), they are developed in an isolated manner and lack systematic analysis and integration of their strengths, hindering further developments.
Investigate-Consolidate-Exploit: A General Strategy for Inter-Task Agent Self-Evolution
This paper introduces Investigate-Consolidate-Exploit (ICE), a novel strategy for enhancing the adaptability and flexibility of AI agents through inter-task self-evolution.
DebugBench: Evaluating Debugging Capability of Large Language Models
Previous evaluations of LLMs' debugging ability are significantly limited by the risk of data leakage, the scale of the dataset, and the variety of tested bugs.
GitAgent: Facilitating Autonomous Agent with GitHub by Tool Extension
As GitHub has hosted a multitude of repositories which can be seen as a good resource for tools, a promising solution is that LLM-based agents can autonomously integrate the repositories in GitHub according to the user queries to extend their tool set.
ML-Bench: Evaluating Large Language Models for Code Generation in Repository-Level Machine Learning Tasks
While Large Language Models (LLMs) have demonstrated proficiency in code generation benchmarks, translating these results into practical development scenarios - where leveraging existing repository-level libraries is the norm - remains challenging.
ProAgent: From Robotic Process Automation to Agentic Process Automation
Empirical experiments are conducted to detail its construction and execution procedure of workflow, showcasing the feasibility of APA, unveiling the possibility of a new paradigm of automation driven by agents.
Rational Decision-Making Agent with Internalized Utility Judgment
Central to the development of rationality is the construction of an internalized utility judgment, capable of assigning numerical utilities to each decision.
AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors
Autonomous agents empowered by Large Language Models (LLMs) have undergone significant improvements, enabling them to generalize across a broad spectrum of tasks.
ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs
Based on ToolBench, we fine-tune LLaMA to obtain an LLM ToolLLaMA, and equip it with a neural API retriever to recommend appropriate APIs for each instruction.
Ranked #3 on
Trajectory Planning
on ToolBench
Exploring Format Consistency for Instruction Tuning
Instruction tuning has emerged as a promising approach to enhancing large language models in following human instructions.
Exploring the Impact of Model Scaling on Parameter-Efficient Tuning
Our investigations reveal that model scaling (1) mitigates the effects of the positions of tunable parameters on performance, and (2) enables tuning methods to achieve performance comparable to full-parameter fine-tuning by optimizing fewer tunable parameters.
Enhancing Chat Language Models by Scaling High-quality Instructional Conversations
Fine-tuning on instruction data has been widely validated as an effective practice for implementing chat language models like ChatGPT.
Recyclable Tuning for Continual Pre-training
In pilot studies, we find that after continual pre-training, the upgraded PLM remains compatible with the outdated adapted weights to some extent.
WebCPM: Interactive Web Search for Chinese Long-form Question Answering
We recruit annotators to search for relevant information using our interface and then answer questions.
Tool Learning with Foundation Models
Considering the lack of a systematic tool learning evaluation in prior works, we experiment with 18 representative tools and show the potential of current foundation models in skillfully utilizing tools.
Language-Specific Representation of Emotion-Concept Knowledge Causally Supports Emotion Inference
Humans no doubt use language to communicate about their emotional experiences, but does language in turn help humans understand emotions, or is language just a vehicle of communication?
FPT: Improving Prompt Tuning Efficiency via Progressive Training
Inspired by these observations, we propose Fast Prompt Tuning (FPT), which starts by conducting PT using a small-scale partial PLM, and then progressively expands its depth and width until the full-model size.
Exploring Mode Connectivity for Pre-trained Language Models
(3) How does the PLM's task knowledge change along the path connecting two minima?
Different Tunes Played with Equal Skill: Exploring a Unified Optimization Subspace for Delta Tuning
To fathom the mystery, we hypothesize that the adaptations of different DETs could all be reparameterized as low-dimensional optimizations in a unified optimization subspace, which could be found by jointly decomposing independent solutions of different DETs.
ProQA: Structural Prompt-based Pre-training for Unified Question Answering
Furthermore, ProQA exhibits strong ability in both continual learning and transfer learning by taking the advantages of the structural prompt.
A Roadmap for Big Model
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models
This necessitates a new branch of research focusing on the parameter-efficient adaptation of PLMs, dubbed as delta tuning in this paper.
ELLE: Efficient Lifelong Pre-training for Emerging Data
We experiment ELLE with streaming data from 5 domains on BERT and GPT.
On Transferability of Prompt Tuning for Natural Language Processing
To explore whether we can improve PT via prompt transfer, we empirically investigate the transferability of soft prompts across different downstream tasks and PLMs in this work.
Exploring Universal Intrinsic Task Subspace via Prompt Tuning
In the experiments, we study diverse few-shot NLP tasks and surprisingly find that in a 250-dimensional subspace found with 100 tasks, by only tuning 250 free parameters, we can recover 97% and 83% of the full prompt tuning performance for 100 seen tasks (using different training data) and 20 unseen tasks, respectively, showing great generalization ability of the found intrinsic task subspace.
bert2BERT: Towards Reusable Pretrained Language Models
However, large language model pre-training costs intensive computational resources and most of the models are trained from scratch without reusing the existing pre-trained models, which is wasteful.
Knowledge Inheritance for Pre-trained Language Models
Specifically, we introduce a pre-training framework named "knowledge inheritance" (KI) and explore how could knowledge distillation serve as auxiliary supervision during pre-training to efficiently learn larger PLMs.
ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning
Pre-trained Language Models (PLMs) have shown superior performance on various downstream Natural Language Processing (NLP) tasks.
CPM: A Large-scale Generative Chinese Pre-trained Language Model
However, applying GPT-3 to address Chinese NLP tasks is still challenging, as the training corpus of GPT-3 is primarily English, and the parameters are not publicly available.
Learning from Explanations with Neural Execution Tree
While deep neural networks have achieved impressive performance on a range of NLP tasks, these data-hungry models heavily rely on labeled data, which restricts their applications in scenarios where data annotation is expensive.
Improving Sequence Modeling Ability of Recurrent Neural Networks via Sememes
Sememes, the minimum semantic units of human languages, have been successfully utilized in various natural language processing applications.
