Paper-Summaries

These are Paper-Summaries and as such they only represents my understanding of the papers and only the information I personally found noteworthy. I recommend reading the papers instead for the complete picture.

2024

Nov. 20
Error and attack tolerance of complex networks
Nov. 18
All Animals Are Equal
Nov. 16
Tearing Down the Internet
Nov. 10
Communication-Efficient Learning of Deep Networks from Decentralized Data
Oct. 31
A Defense of Abortion by Judith Thomson
Oct. 30
Claims of Philosophy by Ayer A. J
Aug. 24
Agentless: Demystifying LLM-based Software Engineering Agents
May. 30
SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering
Apr. 17
AutoCodeRover: Autonomous Program Improvement
Apr. 14
RoCo: Dialectic Multi-Robot Collaboration with Large Language Models
Apr. 06
ReST meets ReAct: Self-Improvement for Multi-Step Reasoning LLM Agent
Apr. 04
REACT : SYNERGIZING REASONING AND ACTING IN LANGUAGE MODELS
Apr. 03
Improving Factuality and Reasoning in Language Models through Multiagent Debate
Apr. 02
Siren’s Song in the AI Ocean:A Survey on Hallucination in Large Language Models
Feb. 05
Deep reinforcement learning from human preferences
Feb. 05
Training language models to follow instructions with human feedback
Feb. 05
Constitutional AI: Harmlessness from AI Feedback
Feb. 04
Mathematical discoveries from program search with large language models
Feb. 04
REFINER: Reasoning Feedback on Intermediate Representations
Feb. 04
Reflexion: Language Agents with Verbal Reinforcement Learning
Feb. 03
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Feb. 02
Self-Instruct: Aligning Language Models with Self-Generated Instructions
Feb. 02
Efficient Estimation of Word Representations in Vector Space
Feb. 01
Toolformer: Language Models Can Teach Themselves to Use Tools
Feb. 01
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Feb. 01
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
Jan. 31
Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning
Jan. 31
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