Search Results for author:
Found 9 papers, 5 papers with code
CodeARC: Benchmarking Reasoning Capabilities of LLM Agents for Inductive Program Synthesis
CodeARC provides a more realistic and challenging testbed for evaluating LLM-based program synthesis and inductive reasoning.
EquiBench: Benchmarking Code Reasoning Capabilities of Large Language Models via Equivalence Checking
Equivalence checking, i. e., determining whether two programs produce identical outputs for all possible inputs, underpins a broad range of applications, including software refactoring, testing, and optimization.
Bridging Context Gaps: Leveraging Coreference Resolution for Long Contextual Understanding
Large language models (LLMs) have shown remarkable capabilities in natural language processing; however, they still face difficulties when tasked with understanding lengthy contexts and executing effective question answering.
Unveiling the Spectrum of Data Contamination in Language Models: A Survey from Detection to Remediation
This survey serves as a succinct overview of the most recent advancements in data contamination research, providing a straightforward guide for the benefit of future research endeavors.
DP-MemArc: Differential Privacy Transfer Learning for Memory Efficient Language Models
Large language models have repeatedly shown outstanding performance across diverse applications.
Tool-Planner: Task Planning with Clusters across Multiple Tools
Experiments show that our approach demonstrates a high pass and win rate across different datasets and optimizes the planning scheme for tool learning in models such as GPT-4 and Claude 3, showcasing the potential of our method.
RA-ISF: Learning to Answer and Understand from Retrieval Augmentation via Iterative Self-Feedback
Large language models (LLMs) demonstrate exceptional performance in numerous tasks but still heavily rely on knowledge stored in their parameters.
SPA: Towards A Computational Friendly Cloud-Base and On-Devices Collaboration Seq2seq Personalized Generation with Casual Inference
Large language models(LLMs) have shown its outperforming ability on various tasks and question answering.
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.
