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Found 13 papers, 7 papers with code
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
Tool learning empowers large language models (LLMs) as agents to use external tools to extend their capability.
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.
