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Found 23 papers, 13 papers with code
Reasoning-to-Defend: Safety-Aware Reasoning Can Defend Large Language Models from Jailbreaking
The reasoning abilities of Large Language Models (LLMs) have demonstrated remarkable advancement and exceptional performance across diverse domains.
Improving Retrieval-Augmented Generation through Multi-Agent Reinforcement Learning
Specifically, we present MMOA-RAG, a Multi-Module joint Optimization Algorithm for RAG, which employs multi-agent reinforcement learning to harmonize all agents' goals towards a unified reward, such as the F1 score of the final answer.
Divide-Then-Aggregate: An Efficient Tool Learning Method via Parallel Tool Invocation
First, we transform traditional tree-based tool search paths into Directed Acyclic Graph (DAG) structure, generating a high-quality parallel tool invocation dataset.
PA-RAG: RAG Alignment via Multi-Perspective Preference Optimization
The emergence of Retrieval-augmented generation (RAG) has alleviated the issues of outdated and hallucinatory content in the generation of large language models (LLMs), yet it still reveals numerous limitations.
MAIR: A Massive Benchmark for Evaluating Instructed Retrieval
Recent information retrieval (IR) models are pre-trained and instruction-tuned on massive datasets and tasks, enabling them to perform well on a wide range of tasks and potentially generalize to unseen tasks with instructions.
MACPO: Weak-to-Strong Alignment via Multi-Agent Contrastive Preference Optimization
As large language models (LLMs) are rapidly advancing and achieving near-human capabilities, aligning them with human values is becoming more urgent.
Retrieving, Rethinking and Revising: The Chain-of-Verification Can Improve Retrieval Augmented Generation
To address these issues, we propose the chain-of-verification (CoV-RAG) to enhance the external retrieval correctness and internal generation consistency.
ATM: Adversarial Tuning Multi-agent System Makes a Robust Retrieval-Augmented Generator
Large language models (LLMs) are proven to benefit a lot from retrieval-augmented generation (RAG) in alleviating hallucinations confronted with knowledge-intensive questions.
Chain of Tools: Large Language Model is an Automatic Multi-tool Learner
Augmenting large language models (LLMs) with external tools has emerged as a promising approach to extend their utility, empowering them to solve practical tasks.
The Real, the Better: Aligning Large Language Models with Online Human Behaviors
Large language model alignment is widely used and studied to avoid LLM producing unhelpful and harmful responses.
Improving the Robustness of Large Language Models via Consistency Alignment
The training process is accomplished by self-rewards inferred from the trained model at the first stage without referring to external human preference resources.
Learning to Use Tools via Cooperative and Interactive Agents
To mitigate these problems, we propose ConAgents, a Cooperative and interactive Agents framework, which coordinates three specialized agents for tool selection, tool execution, and action calibration separately.
KnowTuning: Knowledge-aware Fine-tuning for Large Language Models
To address these problems, we propose a knowledge-aware fine-tuning (KnowTuning) method to improve fine-grained and coarse-grained knowledge awareness of LLMs.
Instruction Distillation Makes Large Language Models Efficient Zero-shot Rankers
Furthermore, our approach surpasses the performance of existing supervised methods like monoT5 and is on par with the state-of-the-art zero-shot methods.
Knowing What LLMs DO NOT Know: A Simple Yet Effective Self-Detection Method
In this paper, we propose a novel self-detection method to detect which questions that a LLM does not know that are prone to generate nonfactual results.
DiQAD: A Benchmark Dataset for End-to-End Open-domain Dialogue Assessment
Dialogue assessment plays a critical role in the development of open-domain dialogue systems.
Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents
In this paper, we first investigate generative LLMs such as ChatGPT and GPT-4 for relevance ranking in IR.
Progressive Adversarial Learning for Bootstrapping: A Case Study on Entity Set Expansion
Bootstrapping has become the mainstream method for entity set expansion.
Element Intervention for Open Relation Extraction
Open relation extraction aims to cluster relation instances referring to the same underlying relation, which is a critical step for general relation extraction.
Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge Bases
Previous literatures show that pre-trained masked language models (MLMs) such as BERT can achieve competitive factual knowledge extraction performance on some datasets, indicating that MLMs can potentially be a reliable knowledge source.
From Bag of Sentences to Document: Distantly Supervised Relation Extraction via Machine Reading Comprehension
By re-organizing all sentences about an entity as a document and extracting relations via querying the document with relation-specific questions, the document-based DS paradigm can simultaneously encode and exploit all sentence-level, inter-sentence-level, and entity-level evidence.
Ranked #1 on
Relationship Extraction (Distant Supervised)
on NYT
Global Bootstrapping Neural Network for Entity Set Expansion
Bootstrapping for entity set expansion (ESE) has been studied for a long period, which expands new entities using only a few seed entities as supervision.
Learning to Bootstrap for Entity Set Expansion
Bootstrapping for Entity Set Expansion (ESE) aims at iteratively acquiring new instances of a specific target category.
