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Found 14 papers, 7 papers with code
Chain of Ideas: Revolutionizing Research Via Novel Idea Development with LLM Agents
Moreover, our CoI agent is budget-friendly, with a minimum cost of \$0. 50 to generate a candidate idea and its corresponding experimental design.
Can We Further Elicit Reasoning in LLMs? Critic-Guided Planning with Retrieval-Augmentation for Solving Challenging Tasks
We validate CR-Planner on challenging domain-knowledge-intensive and reasoning-heavy tasks, including competitive programming, theorem-driven math reasoning, and complex domain retrieval problems.
Auto-Arena: Automating LLM Evaluations with Agent Peer Battles and Committee Discussions
During the peer battles, we observe intriguing scenarios where the LLM candidates display competitive behaviors and even learn from the opponents.
Lifelong Event Detection with Embedding Space Separation and Compaction
However, the simple combination of memory data and new-task samples can still result in substantial forgetting of previously acquired knowledge, which may occur due to the potential overlap between the feature distribution of new data and the previously learned embedding space.
How Much are Large Language Models Contaminated? A Comprehensive Survey and the LLMSanitize Library
With the rise of Large Language Models (LLMs) in recent years, abundant new opportunities are emerging, but also new challenges, among which contamination is quickly becoming critical.
Data Augmentation using Large Language Models: Data Perspectives, Learning Paradigms and Challenges
In the rapidly evolving field of large language models (LLMs), data augmentation (DA) has emerged as a pivotal technique for enhancing model performance by diversifying training examples without the need for additional data collection.
ChatGPT's One-year Anniversary: Are Open-Source Large Language Models Catching up?
Upon its release in late 2022, ChatGPT has brought a seismic shift in the entire landscape of AI, both in research and commerce.
PromptSum: Parameter-Efficient Controllable Abstractive Summarization
Prompt tuning (PT), a parameter-efficient technique that only tunes the additional prompt embeddings while keeping the backbone pre-trained language model (PLM) frozen, has shown promising results in language understanding tasks, especially in low-resource scenarios.
Chain-of-Knowledge: Grounding Large Language Models via Dynamic Knowledge Adapting over Heterogeneous Sources
Specifically, CoK consists of three stages: reasoning preparation, dynamic knowledge adapting, and answer consolidation.
Randomized Smoothing with Masked Inference for Adversarially Robust Text Classifications
Large-scale pre-trained language models have shown outstanding performance in a variety of NLP tasks.
Verify-and-Edit: A Knowledge-Enhanced Chain-of-Thought Framework
As large language models (LLMs) have become the norm in NLP, demonstrating good performance in generation and reasoning tasks, one of its most fatal disadvantages is the lack of factual correctness.
Explaining Language Models' Predictions with High-Impact Concepts
The emergence of large-scale pretrained language models has posed unprecedented challenges in deriving explanations of why the model has made some predictions.
Retrieving Multimodal Information for Augmented Generation: A Survey
As Large Language Models (LLMs) become popular, there emerged an important trend of using multimodality to augment the LLMs' generation ability, which enables LLMs to better interact with the world.
Learning to Initialize: Can Meta Learning Improve Cross-task Generalization in Prompt Tuning?
Despite this, PT has been shown to rely heavily on good initialization of the prompt embeddings.
